US20150313559A1 - System and method for detecting a problem tooth - Google Patents
System and method for detecting a problem tooth Download PDFInfo
- Publication number
- US20150313559A1 US20150313559A1 US14/797,321 US201514797321A US2015313559A1 US 20150313559 A1 US20150313559 A1 US 20150313559A1 US 201514797321 A US201514797321 A US 201514797321A US 2015313559 A1 US2015313559 A1 US 2015313559A1
- Authority
- US
- United States
- Prior art keywords
- tooth
- objects
- data
- processing module
- dental
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000000034 method Methods 0.000 title claims abstract description 86
- 230000012010 growth Effects 0.000 claims abstract description 25
- 238000012545 processing Methods 0.000 claims abstract description 25
- 210000005036 nerve Anatomy 0.000 claims abstract description 18
- 210000003298 dental enamel Anatomy 0.000 claims abstract description 14
- 210000004268 dentin Anatomy 0.000 claims abstract description 14
- 238000002591 computed tomography Methods 0.000 claims abstract description 10
- 230000000694 effects Effects 0.000 claims abstract description 8
- 238000001514 detection method Methods 0.000 claims description 21
- 230000037182 bone density Effects 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000003384 imaging method Methods 0.000 description 15
- 230000008569 process Effects 0.000 description 13
- 238000012800 visualization Methods 0.000 description 10
- 238000003325 tomography Methods 0.000 description 9
- 208000002925 dental caries Diseases 0.000 description 8
- 238000004891 communication Methods 0.000 description 7
- 206010011732 Cyst Diseases 0.000 description 6
- 210000000988 bone and bone Anatomy 0.000 description 6
- 208000031513 cyst Diseases 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 238000013178 mathematical model Methods 0.000 description 5
- 238000009877 rendering Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 239000007943 implant Substances 0.000 description 4
- 210000004357 third molar Anatomy 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 210000004262 dental pulp cavity Anatomy 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 210000004872 soft tissue Anatomy 0.000 description 3
- 210000001519 tissue Anatomy 0.000 description 3
- 210000003484 anatomy Anatomy 0.000 description 2
- 230000002547 anomalous effect Effects 0.000 description 2
- 230000008468 bone growth Effects 0.000 description 2
- 230000037416 cystogenesis Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000002059 diagnostic imaging Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 208000015181 infectious disease Diseases 0.000 description 2
- 238000002595 magnetic resonance imaging Methods 0.000 description 2
- 210000000056 organ Anatomy 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 208000008312 Tooth Loss Diseases 0.000 description 1
- 241000251539 Vertebrata <Metazoa> Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 229910021417 amorphous silicon Inorganic materials 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 210000004204 blood vessel Anatomy 0.000 description 1
- 210000004027 cell Anatomy 0.000 description 1
- 230000001055 chewing effect Effects 0.000 description 1
- 238000004040 coloring Methods 0.000 description 1
- 210000002808 connective tissue Anatomy 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000007123 defense Effects 0.000 description 1
- 229910003460 diamond Inorganic materials 0.000 description 1
- 239000010432 diamond Substances 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 229940079593 drug Drugs 0.000 description 1
- 238000002593 electrical impedance tomography Methods 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 210000004195 gingiva Anatomy 0.000 description 1
- 230000002458 infectious effect Effects 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 210000004400 mucous membrane Anatomy 0.000 description 1
- 210000004126 nerve fiber Anatomy 0.000 description 1
- 210000004416 odontoblast Anatomy 0.000 description 1
- 238000012014 optical coherence tomography Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000002600 positron emission tomography Methods 0.000 description 1
- 229910052704 radon Inorganic materials 0.000 description 1
- SYUHGPGVQRZVTB-UHFFFAOYSA-N radon atom Chemical compound [Rn] SYUHGPGVQRZVTB-UHFFFAOYSA-N 0.000 description 1
- 238000002603 single-photon emission computed tomography Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T19/00—Manipulating 3D models or images for computer graphics
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/14—Applications or adaptations for dentistry
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/02—Devices for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
- A61B6/03—Computerised tomographs
- A61B6/032—Transmission computed tomography [CT]
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/46—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with special arrangements for interfacing with the operator or the patient
- A61B6/461—Displaying means of special interest
- A61B6/463—Displaying means of special interest characterised by displaying multiple images or images and diagnostic data on one display
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/46—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with special arrangements for interfacing with the operator or the patient
- A61B6/467—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment with special arrangements for interfacing with the operator or the patient characterised by special input means
-
- A61B6/51—
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
- A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B6/00—Apparatus for radiation diagnosis, e.g. combined with radiation therapy equipment
- A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
- A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
- A61B6/5223—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data generating planar views from image data, e.g. extracting a coronal view from a 3D image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
- G06T2200/24—Indexing scheme for image data processing or generation, in general involving graphical user interfaces [GUIs]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
- G06T2207/30036—Dental; Teeth
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/03—Recognition of patterns in medical or anatomical images
Definitions
- the present invention relates to a system and method for detecting a problem tooth. More specifically, the present invention is related to mathematically modeling the growth of at least one tooth object, the decay for at least one tooth object, or the combination thereof.
- dental images are displayed in two-dimensions using light tables, e.g. X-rays. These two dimensional views provide a single perspective of the image.
- Three-dimensional (3-D) imaging systems have also been developed. These systems provide high-definition digital imaging with relatively short scan times, e.g. 20 seconds.
- the image reconstruction takes less than two minutes.
- the X-ray source is typically a high frequency source with a cone x-ray beam, and employs an image detector with an amorphous silicon flat panel.
- the images are 12-bit gray scale and may have a voxel size of 0.4 mm to 0.1 mm.
- Image acquisition is performed in a single session and is based on a 360 degree rotation of the X-ray source.
- the output data are digital images that are stored using conventional imaging formats such as the Digital Imaging and Communications in Medicine (DICOM) standard.
- DICOM Digital Imaging and Communications in Medicine
- the 3-D volumetric imaging system provides complete views of oral and maxillofacial structures.
- the volumetric images provide complete 3-D views of anatomy for a more thorough analysis of bone structure and tooth orientation. These 3-D images are frequently used for implant and oral surgery, orthodontics, and TMJ analysis.
- the software techniques for visualization of the dental images do not provide a dentist with sufficient flexibility to manipulate the 3-D image. Additionally, the visualization features provided by current third party solutions lack the ability to detect objects, detect irregularities, and detect anomalies.
- a system and method for visualizing a dental image that includes a plurality of high resolution dental data, a plurality of tooth objects, at least one threshold and a processing module is described.
- the plurality of high resolution dental data is generated using computed tomography.
- the plurality of tooth objects selected for each tooth from the dental data includes at least one of an enamel object, a dentin object, a pulp object, a root object, and a nerve object.
- the at least one threshold is used to detect at least one problem tooth.
- the processing module detects at least one problem tooth. Additionally, the processing module mathematically models the growth of at least one tooth object, the decay for at least one tooth object, or the combination thereof. Furthermore, the processing module determines the effect the problem tooth has on at least one other tooth object.
- the system and method includes a database that further includes a plurality of data fields that include a plurality of standard shapes associated with each tooth and a plurality of bone density data for each section of tooth. Additionally, the database includes a plurality of normative standards and at least one statistical standard for anomaly detection.
- the system and method includes identifying a common boundary between at least two tooth objects.
- the system and method includes identifying a particular tooth for further analysis. Also, the system and method includes analyzing the tooth objects for the particular tooth. Furthermore, the system and method includes analyzing the particular tooth object by slicing the tooth object at one or more locations.
- FIG. 1A is shows an illustrative system overview.
- FIG. 1B is an illustrative general purpose computer.
- FIG. 1C is an illustrative client-server system.
- FIG. 2 is an illustrative raw image.
- FIG. 3 is an illustrative object identification flowchart.
- FIG. 4A is an illustrative drawing showing jaw object identification.
- FIG. 4B is an illustrative drawing showing tooth object identification.
- FIG. 5 is an illustrative 3-D image of a tooth object.
- FIG. 6 is an illustrative first slice of the tooth object in FIG. 5 .
- FIG. 7 is an illustrative second slice of the tooth object in FIG. 5 .
- FIG. 8 is an illustrative third slice of the tooth object in FIG. 5 .
- FIG. 9 is an illustrative flowchart for anomaly detection and for modeling growth rates.
- FIGS. 10A and 10B shows a normal orientation for a wisdom tooth.
- FIGS. 11A and 11B shows the beginning phase of horizontal impaction.
- FIGS. 12A and 12B shows an illustrative example of cyst formation.
- FIGS. 13A and 13B shows an illustrative example of cyst growth.
- FIGS. 14A and 14B shows the resulting tooth decay and continuing cyst growth.
- tomographic imaging includes analyzing the attenuation of the captured image using the Radon transform and filtered back projection.
- tomography there are a variety of different types of tomography including but not limited to Atom Probe Tomography, Computed Tomography, Electrical Impedance Tomography, Magnetic Resonance Tomography, Optical Coherence Tomography, Positron Emission Tomography, Quantum Tomography, Single Photon Emission Computed Tomography, and X-Ray Tomography. Attenuation refers to any reduction in signal strength.
- Visualization refers to the process of taking one or more images and incorporates a comprehension of the physical relationship or significance of the features contained in the images.
- An object is a physical relationship within an image that is capable of being grasped through visualization and an object is comprised of a plurality of voxels that presume a common basis.
- a variety of techniques, methods, algorithms, mathematical formulae, or any combination thereof may be used to identify a common basis.
- elements such as location, bone density, shape or a combination thereof may be used to identify at least one common basis that is used for object identification. Bone density is the measure of mass of bone in relation to volume. Therefore, one or more common basis may be used for object identification.
- a modality in a medical image is any of the various types of equipment or probes used to acquire images of the body.
- Magnetic Resonance Imaging is an example of a modality in this context.
- the illustrative system 200 receives a 3-D dental image 202 that is stored in a first database 204 that stores archived images.
- a digital acquisition and processing component 208 processes received 3-D dental images.
- Particular information that is used to process the 3-D dental images is stored in the second database 206 .
- An interactive graphical user interface 210 permits a user to manipulate the processed images and to interact with each illustrative dental object.
- the 3-D dental image is generated by a medical imaging device such as an i-CAT 3-D Imaging System from Imaging Sciences International.
- the databases 204 and 206 comprise a plurality of data fields including, but not limited to, data fields that correspond to the location for a plurality of teeth, a plurality of locations for each section of tooth, a plurality of standard shapes associated with each tooth, a plurality of standard shapes associated with each of the sections of tooth, and a plurality of bone density data for each section of tooth.
- the digital processing component 208 is configured to process the 3-D image, and is in operative communication with the database.
- the digital processing component is configured to provide improved visualization of the medical image.
- the digital processing component 208 is configured to identify an object by combining a plurality of voxels having a common density and tagging the object using the methods described herein.
- a voxel is a volume element that represents a value in 3-D space.
- Common density is a density associated with a particular object in an image, in which a degree of attenuation within the image is associated with density.
- the digital processing component 208 is also configured to permit modifying the shape of at least one object. Furthermore, the digital processing component 208 is configured to provide a method for detecting anomalies and mathematically modeling growth rates.
- the digital processing component 208 is a computer having a processor as shown in FIG. 1B .
- the illustrative general purpose computer 10 is suitable for implementing the systems and methods described herein.
- the general purpose computer 10 includes at least one central processing unit (CPU) 12 , a display such as monitor 14 , and an input device 15 such as cursor control device 16 or keyboard 17 .
- the cursor control device 16 can be implemented as a mouse, a joy stick, a series of buttons, or any other input device which allows user to control the position of a cursor or pointer on the display monitor 14 .
- Another illustrative input device is the keyboard 17 .
- the general purpose computer may also include random access memory ⁇ RAM) 18 , hard drive storage 20 , read-only memory (ROM) 22 , a modem 26 and a graphic co-processor 28 . All of the elements of the general purpose computer 10 may be tied together by a common bus 30 for transporting data between the various elements.
- RAM random access memory
- ROM read-only memory
- modem modem
- graphic co-processor 28 All of the elements of the general purpose computer 10 may be tied together by a common bus 30 for transporting data between the various elements.
- the bus 30 typically includes data, address, and control signals.
- the general purpose computer 10 illustrated in FIG. 1B includes a single data bus 30 which ties together all of the elements of the general purpose computer 10 , there is no requirement that there be a single communication bus which connects the various elements of the general purpose computer 10 .
- the CPU 12 , RAM 18 , ROM 22 , and graphics co-processor might be tied together with a data bus while the hard disk 20 , modem 26 , keyboard 24 , display monitor 14 , and cursor control device are connected together with a second data bus (not shown).
- the first data bus 30 and the second data bus could be linked by a bi-directional bus interface (not shown).
- the elements such as the CPU 12 and the graphics coprocessor 28 could be connected to both the first data bus 30 and the second data bus and communication between the first and second data bus would occur through the CPU 12 and the graphics co-processor 28 .
- the methods of the present invention are thus executable on any general purpose computing architecture, but there is no limitation that this architecture is the only one which can execute the methods of the present invention.
- BioImage and BioPSE Power App is a visualization and analysis application developed by the University of Utah that may run on the computer 10 .
- the software programs explore scalar data sets such as medical imaging volumes. In operation, the user chooses an input data set.
- BioImage supports a variety of different industry standard formats including DICOM and Analyze. For example, a dental data set containing a single tooth may be loaded into these programs.
- the illustrative software program permits the user to resample, crop, histogram or median filter the data. Using a cropping filter permits visually removing the excess data from the borders of the volume.
- the GUI permits the user to explore the data volume in both 2-D and 3-D using the rendering panes in the software.
- the software also permits slice views wherein the user can change slices and can adjust the contrast and brightness of the data.
- Yet another feature of BioImage is the volume rendering engine. From the volume rendering tab, the user turns on the direct volume rendering visualization.
- the volume rendering algorithm uses a transfer function to assign color and opacity based on both data values and gradient magnitudes of the volume.
- the dentin is a calcified tissue of the body, and along with enamel, cementum and pulp are the four major components of teeth. Pulp is the part in the center of a tooth make up of living soft tissue and cells called odontoblasts.
- the methods described herein may use a client/server architecture which is shown in FIG. 1C .
- the client/server architecture 50 can be configured to perform similar functions as those performed by the general purpose computer 10 .
- In the client-server architecture communication generally takes the form of a request message 52 from a client 54 to the server 56 asking for the server 56 to perform a server process 58 .
- the server 56 performs the server process 58 and sends back a reply 60 to a client process 62 resident within client 54 .
- Additional benefits from use of a client/server architecture include the ability to store and share gathered information and to collectively analyze gathered information.
- a peer-to-peer network (not shown) can used to implement the methods described herein.
- the general purpose computer I 0 In operation, the general purpose computer I 0 , client/server network system 50 , or peer-to-peer network system execute a sequence of machine-readable instructions. These machine readable instructions may reside in various types of signal bearing media.
- one aspect of the present invention concerns a programmed product, comprising signal-bearing media tangibly embodying a program of machine-readable instructions executable by a digital data processor such as the CPU 12 for the general purpose computer 10 .
- the computer readable medium may comprise, for example, RAM 18 contained within the general purpose computer 10 or within a server 56 .
- the computer readable medium may be contained in another signal-bearing media, such as a magnetic data storage diskette that is directly accessible by the general purpose computer 10 or the server 56 .
- the machine readable instructions within the computer readable medium may be stored in a variety of machine readable data storage media, such as a conventional “hard drive” or a RAID array, magnetic tape, electronic read-only memory (ROM), an optical storage device such as CD-ROM, DVD, or other suitable signal bearing media including transmission media such as digital and analog and communication links.
- the machine-readable instructions may comprise software object code from a programming language such as C++, Java, or Python.
- FIG. 2 there is shown an illustrative raw image of a mouth and a tooth.
- FIG. 2 provides a visual aid of the basic anatomy of the mouth and the tooth similar to what may be generated using the illustrative i-CAT imaging system described above.
- This visual aid has many limitations, namely, the multiple objects in the image have not been identified. Additionally, the image is essentially a raw image that has not been standardized using some type of calibrated sample.
- the illustrative method 70 for visualizing a 3-D medical image comprises receiving a plurality of high resolution 3-D medical data at block 72 that are generated using a tomography technique, e.g. computed tomography (CT) scans.
- CT computed tomography
- the method for visualizing a dental image comprises receiving a plurality of high resolution 3-D dental data associated with a patient's mouth that is generated using x-ray tomography.
- the high resolution 3-D data received at block 72 includes a standard for calibration purposes.
- the standard may have a particular density that can be associated with a bone density.
- the standard is composed of a material that can be associated with the bone density of an illustrative tooth.
- the standard may placed adjacent to the patient and held physically or mechanically in place. Alternatively, the patient may place the standard in the mouth and bite the standard.
- the method then proceeds to block 74 where the high resolution data is converted into an image comprised of a plurality of cubic voxels.
- the method proceeds to identify the location for each cubic voxel.
- the method then proceeds to identify a degree of attenuation for the voxels at block 78 . Attenuation is the reduction in amplitude and intensity of a signal.
- the method associates a common density with the degree of attenuation.
- the method also associates the degree of attenuation for the standard with the previously determined standard density related to block 72 .
- the degree of attenuation for each voxel is associated with at least one of a plurality of common bone densities.
- the method then proceeds to block 82 that identifies an object by combining the voxels having the common density and determines an initial shape for the object.
- the identifying of the object may comprise comparing the initial shape of the object to a standard shape.
- the common density may also be modified by a user, thereby resulting in the object having a different shape.
- the 3-D data may be dental data and the common density is a bone density associated with teeth, mouth, or jaw.
- the method then proceeds to generate dental objects by combining voxels having one of the common bone densities. The boundaries for each dental object are also determined.
- Various opportunities may be presented where each dental object is compared to a standard shape to confirm identification of each dental object.
- the method then proceeds to block 84 where at least one object is then tagged for further analysis 78 .
- the tagged objects in the tooth may be associated with a “metatag” so that each object can be quickly identified and viewed.
- a “metatag” as used herein refers to a “tag” that is associated with each object, wherein the “tag” is searchable and is used to provide a structured means for identifying object so that the “tagged” object or objects can be viewed.
- the tagged object may then be extracted for further analysis.
- the extracted object may then be viewed using a plurality of different perspectives.
- the tagged and extracted object can then be viewed by slicing the object at desired locations.
- An illustrative object may be a particular tooth object, an enamel object, a dentin object, a pulp object, a root object, a nerve object, or any other such dental object associated with mouth and jaw.
- the plurality of objects may also be identified such as teeth objects, a plurality of nerve objects, and a plurality of bone objects. More generally, a plurality of objects may also be identified by combining the voxels having one of a plurality of different common densities and by determining the boundaries for each object.
- each of the objects is then compared to a standard shape, and each of the objects is then tagged to permit one or more objects to be combined.
- the plurality of objects may also be identified such as teeth objects, a plurality of nerve objects, a plurality of bone objects, or any other such object.
- the flowchart also describes modifying the shape of at least one object in a scanned 3-D medical image at block 88 .
- a common boundary between the first object and the second object is identified.
- the common boundary is configured to identify a change in bone density between the first object and the second object.
- the method permits a user to modify the common boundary by permitting the user to modify the apparent bone density of the first object.
- the method also provides for coloring each voxel according to each of the bone densities.
- the common boundary spans a relatively broad area when there is little change in bone density between the first object and the second object.
- the method also permits evaluating a plurality of standard shapes when generating the first object and the second object.
- each of the plurality of objects may have a plurality of tags, in which each tag may be extracted from the image as represented by block 90 .
- the first object is tagged as a first tooth and the second object is a second tooth.
- the first object and said second object is selected from a group consisting of a tooth object, an enamel object, a dentin object, a pulp object, a root object, a nerve object, a plurality of teeth objects, a plurality of nerve objects, or a plurality of bone objects.
- the method 70 also supports performing imaging operation such as slicing objects as represented by block 92 and described in further detail below.
- the method involves known physiologies discovered by the method above and supports analyzing relevant materials.
- a 3-D DICOM file is converted to 3-D volumetric image, if the process has not already been completed, the volume is oriented according to axes, body portion contained, scale, etc.
- Object identification may be performed as a function of common densities, density transitions, and known or standard shape similarities.
- a map of the objects can then be created and displayed.
- the systems and method described may be applied to non-specific objects, issue identification by density, adjacent material, and general location.
- Margin (junction) shape determination, specific material shape (object) determination based on material profile, and cataloging of same may also be performed.
- objects, passageways, etc. e.g. teeth, nerve canals, implants, vertebrae, jaw, etc.
- the objective is to identify recurring examples of similar objects such as teeth, and to catalog their identification, both by normative standards and by reference to statistically compiled identifiers and shape.
- FIG. 4A there is shown an illustrative drawing 100 with dental and jaw object identification.
- the tagged objects in the tooth are associated with a “metatag” or “searchable tag” so that each object can be quickly identified and viewed.
- a variety of soft tissues objects such as nerve objects are shown.
- the nerve objects refer to sensitive tissue in the pulp of a tooth, or any bundle of nerve fibers running to various organs in the body.
- a standard 102 for calibration purposes is shown. By way of example and not of limitation, these nerve objects are typically identified using MRI or CT scans.
- FIG. 5 there is shown an illustrative 3-D image of an illustrative tooth object 120 .
- the tooth object 120 comprises each of the objects described above such as the enamel, dentin and pulp.
- a variety of different slices of the 3-D image are presented.
- FIG. 6 provides an illustrative first slice 122 of the tooth object in FIG. 5 .
- FIG. 7 provides an illustrative second slice 124 of the tooth object in FIG. 5
- FIG. 8 is an illustrative third slice 126 of the tooth object.
- Each of these drawings depict that the tagged objects can be “sliced” to provide a clearer view of the particular tooth. This slicing process may also be used for anomaly detection as described below.
- FIG. 9 there is shown an illustrative flowchart for anomaly detection and for modeling growth rates that is a continuation of the flowchart in FIG. 3 .
- An anomaly is a deviation or departure from a normal, or common order, or form, or rule, and is generally used to refer to a substantial defect.
- An “irregularity” is distinguishable from an anomaly since “irregular” simply means lacking symmetry, evenness, or having a minor defect.
- the flowchart describes a method for identifying anomalies in a scanned 3-D dental image. The method accesses a database 206 (shown in FIG.
- IA having a plurality of data fields related to location for a plurality of teeth, a plurality of locations for each section of tooth, a plurality of standard shapes associated with the teeth, a plurality of standard shapes associated with each of the sections of tooth, and a plurality of bone density data for each section of tooth.
- a 3-D image having a plurality of cubic voxels is generated, and the location for each voxel is identified.
- the method then proceeds to identify a signal strength for each cubic voxel, and associates the signal strength for each voxel with the bone density data.
- Signal strength refers to the total amount of power of RF received by the receiver. This is divided into useful signal, referred to as EC/IO, and the noise floor.
- the method at block 92 performs object identification at block 132 where an illustrative first object is generated by combining a first grouping of voxels having a first bone density.
- the illustrative first object is compared to objects in the database 206 (shown in FIG. IA).
- the method then proceeds to identify irregularities at block 136 .
- anomalies are identified after comparing the first object to one or more fields in the database 206 .
- the database comprises a plurality of normative standards and statistical standards for anomaly detection that distinguished between anomalies and irregularities.
- the anomaly detection at block 138 may also comprise generating a plurality of other objects and tagging the objects so that one or more objects may be combined.
- the method may then proceed to identify one or more anomalies associated with the plurality of objects.
- the method supports identifying one or more anomalies associated with at least one object that is tagged as a tooth object, in which the tooth object further comprises a plurality of tagged objects selected from a group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object.
- teeth are not the only objects that grow and it shall be appreciated that the systems and methods described herein may be used to model bone growth and bone decay in general. Growth rate projections may be based on such parameters as age, gender, height, weight, ethnicity, and other such parameters that may be valuable to mathematically modeling growth rates. Those skilled in the art shall appreciate that measurements such as bone growth are also primary indicators and are provided for illustrative purposes only.
- the relational effects resulting from having modeled the growth of a particular object are determined.
- the modeled growth results in changes to the local conditions, and these changes are presented to the user.
- the method then proceeds to decision diamond 144 where the determination of whether to change any of the parameters described above is necessary. Therefore, modeled growth rates may be changed, thresholds for anomaly detection may be changed, and the basis for object identification may also be modified.
- At least one expected growth rate is provided for at least one tooth.
- the method then proceeds to mathematically model a growth rate for the first tooth object using the expected growth rate, and modifies the location of a plurality of objects surrounding the first tooth object due to the growth of the first tooth object.
- the method may then proceed to identify an anomaly after comparing the first object to one or more fields in the database.
- Objects may then be tagged so that so that one or more objects may be combined.
- Anomalies may then be associated with one or more tooth objects selected from a group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object.
- anomaly detection may be performed by identifying at least one threshold for anomaly detection. The gathered data is then compared to the threshold to determine if one or more anomalies have been detected.
- the potential anomaly may also be associated with a first mathematical model, which is then compared to a second “normative” mathematical model using recently extracted data.
- the first mathematical model may have variables that can be modified, which mirrors the ability to modify the object.
- the correlation between the first mathematical model and second mathematical model is determined by a correlation estimate that may be based on the concordances of randomly sampled pairs.
- the method may also provide for the use of clustering analysis.
- Clustering provides an additional method for analyzing the data.
- Spatial cluster detection has two objectives, namely, to identify the locations, shapes and sizes of potentially anomalous spatial regions, and to determine whether each of these potential clusters is more likely to a valid cluster or simply a chance cluster.
- the process of spatial cluster detection can separated into two parts: first, determining the expected result, secondly, determining which regions deviate from the expected result.
- the process of determining which regions deviate from the expected result can be performed using a variety of techniques. For example, simple statistics can be used to determine a number of spatial standard deviations, and anomalies simply fall outside the standard deviations.
- spatial scan statistics can be used as described by Kulldorff. (M. Kulldorff. A Spatial Scan Statistic. Communications in Statistics: Theory and Methods 26(6), 1481-1496, 1997.) In this method, a given set of spatial regions are searched and regions are found using hypothesis testing.
- a generalized spatial scan framework can also be used. (M. R. Sabhnani, D. B. Neill, A. W. Moore, F.-C. tsui, M. M. Wagner, and J. U. Espino. Detecting anomalous patterns in pharmacy retail data. KDD Workshop on Data Mining Methods for Anomaly Detection, 2005.)
- an anomaly 150 is detected in FIG. 11A and the exploded view in FIG. 11B .
- the anomaly reflects that this is a problem tooth, and this anomaly can immediately be brought to the physician's attention using the systems and method described herein.
- This image also shows the beginning phase of horizontal impaction, and the formation of a cyst.
- a cyst is an abnormal membranous sac containing a gaseous, liquid, or semisolid substance. Additionally, at location there is shown a dental cavity/caries that are just starting.
Abstract
A system and method for visualizing a dental image that includes a plurality of high resolution dental data, a plurality of tooth objects, at least one threshold and a processing module is described. The plurality of high resolution dental data that is generated using computed tomography. The plurality of tooth objects selected for each tooth from the dental data includes at least one of an enamel object, a dentin object, a pulp object, a root object, and a nerve object. The at least one threshold is used to detect at least one problem tooth. The processing module detects at least one problem tooth. Additionally, the processing module mathematically models the growth of at least one tooth object, the decay for at least one tooth object, or the combination thereof. Furthermore, the processing module determines the effect the problem tooth has on at least one other tooth object.
Description
- This patent application is a continuation of patent application Ser. No. 11/890,533 filed on Aug. 6, 2007, which claims the benefit of
provisional patent application 60/837,311, filed Aug. 11, 2006, all of which are incorporated herein by reference in their entirety. - The present invention relates to a system and method for detecting a problem tooth. More specifically, the present invention is related to mathematically modeling the growth of at least one tooth object, the decay for at least one tooth object, or the combination thereof.
- Generally, dental images are displayed in two-dimensions using light tables, e.g. X-rays. These two dimensional views provide a single perspective of the image. Three-dimensional (3-D) imaging systems have also been developed. These systems provide high-definition digital imaging with relatively short scan times, e.g. 20 seconds. The image reconstruction takes less than two minutes. The X-ray source is typically a high frequency source with a cone x-ray beam, and employs an image detector with an amorphous silicon flat panel. The images are 12-bit gray scale and may have a voxel size of 0.4 mm to 0.1 mm. Image acquisition is performed in a single session and is based on a 360 degree rotation of the X-ray source. The output data are digital images that are stored using conventional imaging formats such as the Digital Imaging and Communications in Medicine (DICOM) standard.
- The 3-D volumetric imaging system provides complete views of oral and maxillofacial structures. The volumetric images provide complete 3-D views of anatomy for a more thorough analysis of bone structure and tooth orientation. These 3-D images are frequently used for implant and oral surgery, orthodontics, and TMJ analysis. There are a variety of different software solutions that can be integrated into the 3-D dental imaging systems. These third party solutions are generally related to implant planning, and assist in planning and placement of the implants. Additionally, the 3-D dental images can be used for developing models to assist in planning an operation.
- In spite of the advances in the 3-D imaging systems and the 3-D imaging software, the software techniques for visualization of the dental images do not provide a dentist with sufficient flexibility to manipulate the 3-D image. Additionally, the visualization features provided by current third party solutions lack the ability to detect objects, detect irregularities, and detect anomalies.
- A system and method for visualizing a dental image that includes a plurality of high resolution dental data, a plurality of tooth objects, at least one threshold and a processing module is described. The plurality of high resolution dental data is generated using computed tomography. The plurality of tooth objects selected for each tooth from the dental data includes at least one of an enamel object, a dentin object, a pulp object, a root object, and a nerve object. The at least one threshold is used to detect at least one problem tooth. The processing module detects at least one problem tooth. Additionally, the processing module mathematically models the growth of at least one tooth object, the decay for at least one tooth object, or the combination thereof. Furthermore, the processing module determines the effect the problem tooth has on at least one other tooth object.
- In one illustrative embodiment, the system and method includes a database that further includes a plurality of data fields that include a plurality of standard shapes associated with each tooth and a plurality of bone density data for each section of tooth. Additionally, the database includes a plurality of normative standards and at least one statistical standard for anomaly detection.
- In another illustrative embodiment, the system and method includes identifying a common boundary between at least two tooth objects.
- In yet another illustrative embodiment, the system and method includes identifying a particular tooth for further analysis. Also, the system and method includes analyzing the tooth objects for the particular tooth. Furthermore, the system and method includes analyzing the particular tooth object by slicing the tooth object at one or more locations.
- Embodiments for the following description are shown in the following drawings:
-
FIG. 1A is shows an illustrative system overview. -
FIG. 1B is an illustrative general purpose computer. -
FIG. 1C is an illustrative client-server system. -
FIG. 2 is an illustrative raw image. -
FIG. 3 is an illustrative object identification flowchart. -
FIG. 4A is an illustrative drawing showing jaw object identification. -
FIG. 4B is an illustrative drawing showing tooth object identification. -
FIG. 5 is an illustrative 3-D image of a tooth object. -
FIG. 6 is an illustrative first slice of the tooth object inFIG. 5 . -
FIG. 7 is an illustrative second slice of the tooth object inFIG. 5 . -
FIG. 8 is an illustrative third slice of the tooth object inFIG. 5 . -
FIG. 9 is an illustrative flowchart for anomaly detection and for modeling growth rates. -
FIGS. 10A and 10B shows a normal orientation for a wisdom tooth. -
FIGS. 11A and 11B shows the beginning phase of horizontal impaction. -
FIGS. 12A and 12B shows an illustrative example of cyst formation. -
FIGS. 13A and 13B shows an illustrative example of cyst growth. -
FIGS. 14A and 14B shows the resulting tooth decay and continuing cyst growth. - In the following detailed description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the spirit and scope of the claims. The following detailed description is, therefore, not to be taken in a limited sense.
- Note, the leading digit(s) of the reference numbers in the Figures correspond to the figure number, with the exception that identical components which appear in multiple figures are identified by the same reference numbers.
- The systems and methods described herein are generally related to visualization tools that operate with 3-D images generated using tomography. Tomography is imaging by sections or sectioning. The mathematical procedures for imaging are referred to as tomographic reconstruction. Imaging is the process of creating a virtual image of a physical object, its detailed structure, its substructure or any combination thereof. Those skilled in the art shall appreciate that tomographic imaging includes analyzing the attenuation of the captured image using the Radon transform and filtered back projection. There are a variety of different types of tomography including but not limited to Atom Probe Tomography, Computed Tomography, Electrical Impedance Tomography, Magnetic Resonance Tomography, Optical Coherence Tomography, Positron Emission Tomography, Quantum Tomography, Single Photon Emission Computed Tomography, and X-Ray Tomography. Attenuation refers to any reduction in signal strength.
- The systems and methods described herein allow improved visualization, object identification, anomaly detection, and predictive growth rate features. Visualization refers to the process of taking one or more images and incorporates a comprehension of the physical relationship or significance of the features contained in the images. An object is a physical relationship within an image that is capable of being grasped through visualization and an object is comprised of a plurality of voxels that presume a common basis. A variety of techniques, methods, algorithms, mathematical formulae, or any combination thereof may be used to identify a common basis. In the illustrative examples, elements such as location, bone density, shape or a combination thereof may be used to identify at least one common basis that is used for object identification. Bone density is the measure of mass of bone in relation to volume. Therefore, one or more common basis may be used for object identification.
- It shall be appreciated by those of ordinary skill in the art that the systems and methods described herein can be applied to a plurality of different modalities. A modality in a medical image is any of the various types of equipment or probes used to acquire images of the body. Magnetic Resonance Imaging is an example of a modality in this context.
- Referring to
FIG. 1A there is shown an illustrative system. Theillustrative system 200 receives a 3-Ddental image 202 that is stored in afirst database 204 that stores archived images. A digital acquisition andprocessing component 208 processes received 3-D dental images. Particular information that is used to process the 3-D dental images is stored in thesecond database 206. An interactivegraphical user interface 210 permits a user to manipulate the processed images and to interact with each illustrative dental object. By way of example and not of limitations, the 3-D dental image is generated by a medical imaging device such as an i-CAT 3-D Imaging System from Imaging Sciences International. - The
databases - The
digital processing component 208 is configured to process the 3-D image, and is in operative communication with the database. The digital processing component is configured to provide improved visualization of the medical image. Thedigital processing component 208 is configured to identify an object by combining a plurality of voxels having a common density and tagging the object using the methods described herein. A voxel is a volume element that represents a value in 3-D space. Common density is a density associated with a particular object in an image, in which a degree of attenuation within the image is associated with density. - Additionally, the
digital processing component 208 is also configured to permit modifying the shape of at least one object. Furthermore, thedigital processing component 208 is configured to provide a method for detecting anomalies and mathematically modeling growth rates. - In one embodiment the
digital processing component 208 is a computer having a processor as shown inFIG. 1B . The illustrativegeneral purpose computer 10 is suitable for implementing the systems and methods described herein. Thegeneral purpose computer 10 includes at least one central processing unit (CPU) 12, a display such asmonitor 14, and aninput device 15 such ascursor control device 16 orkeyboard 17. Thecursor control device 16 can be implemented as a mouse, a joy stick, a series of buttons, or any other input device which allows user to control the position of a cursor or pointer on thedisplay monitor 14. Another illustrative input device is thekeyboard 17. The general purpose computer may also include random access memory {RAM) 18,hard drive storage 20, read-only memory (ROM) 22, amodem 26 and agraphic co-processor 28. All of the elements of thegeneral purpose computer 10 may be tied together by acommon bus 30 for transporting data between the various elements. - The
bus 30 typically includes data, address, and control signals. Although thegeneral purpose computer 10 illustrated inFIG. 1B includes asingle data bus 30 which ties together all of the elements of thegeneral purpose computer 10, there is no requirement that there be a single communication bus which connects the various elements of thegeneral purpose computer 10. For example, theCPU 12,RAM 18,ROM 22, and graphics co-processor might be tied together with a data bus while thehard disk 20,modem 26, keyboard 24, display monitor 14, and cursor control device are connected together with a second data bus (not shown). In this case, thefirst data bus 30 and the second data bus could be linked by a bi-directional bus interface (not shown). Alternatively, some of the elements, such as theCPU 12 and thegraphics coprocessor 28 could be connected to both thefirst data bus 30 and the second data bus and communication between the first and second data bus would occur through theCPU 12 and thegraphics co-processor 28. The methods of the present invention are thus executable on any general purpose computing architecture, but there is no limitation that this architecture is the only one which can execute the methods of the present invention. - Various visualization and analysis application may be run on the illustrative
general purpose computer 10. For example, BioImage and BioPSE Power App is a visualization and analysis application developed by the University of Utah that may run on thecomputer 10. The software programs explore scalar data sets such as medical imaging volumes. In operation, the user chooses an input data set. BioImage supports a variety of different industry standard formats including DICOM and Analyze. For example, a dental data set containing a single tooth may be loaded into these programs. - After the data is loaded, the illustrative software program permits the user to resample, crop, histogram or median filter the data. Using a cropping filter permits visually removing the excess data from the borders of the volume. The GUI permits the user to explore the data volume in both 2-D and 3-D using the rendering panes in the software.
- The software also permits slice views wherein the user can change slices and can adjust the contrast and brightness of the data. Yet another feature of BioImage is the volume rendering engine. From the volume rendering tab, the user turns on the direct volume rendering visualization. The volume rendering algorithm uses a transfer function to assign color and opacity based on both data values and gradient magnitudes of the volume. Thus, the interface between the dentin and pulp of the tooth may be colored differently. The dentin is a calcified tissue of the body, and along with enamel, cementum and pulp are the four major components of teeth. Pulp is the part in the center of a tooth make up of living soft tissue and cells called odontoblasts.
- Alternatively, the methods described herein may use a client/server architecture which is shown in
FIG. 1C . It shall be appreciated by those of ordinary skill in the art that the client/server architecture 50 can be configured to perform similar functions as those performed by thegeneral purpose computer 10. In the client-server architecture communication generally takes the form of arequest message 52 from aclient 54 to theserver 56 asking for theserver 56 to perform aserver process 58. Theserver 56 performs theserver process 58 and sends back areply 60 to aclient process 62 resident withinclient 54. Additional benefits from use of a client/server architecture include the ability to store and share gathered information and to collectively analyze gathered information. In another alternative embodiment, a peer-to-peer network (not shown) can used to implement the methods described herein. - In operation, the general purpose computer I 0, client/
server network system 50, or peer-to-peer network system execute a sequence of machine-readable instructions. These machine readable instructions may reside in various types of signal bearing media. In this respect, one aspect of the present invention concerns a programmed product, comprising signal-bearing media tangibly embodying a program of machine-readable instructions executable by a digital data processor such as theCPU 12 for thegeneral purpose computer 10. - It shall be appreciated by those of ordinary skill that the computer readable medium may comprise, for example,
RAM 18 contained within thegeneral purpose computer 10 or within aserver 56. Alternatively, the computer readable medium may be contained in another signal-bearing media, such as a magnetic data storage diskette that is directly accessible by thegeneral purpose computer 10 or theserver 56. Whether contained in the general purpose computer or in the server, the machine readable instructions within the computer readable medium may be stored in a variety of machine readable data storage media, such as a conventional “hard drive” or a RAID array, magnetic tape, electronic read-only memory (ROM), an optical storage device such as CD-ROM, DVD, or other suitable signal bearing media including transmission media such as digital and analog and communication links. In an illustrative embodiment, the machine-readable instructions may comprise software object code from a programming language such as C++, Java, or Python. - Referring to
FIG. 2 there is shown an illustrative raw image of a mouth and a tooth. In general,FIG. 2 provides a visual aid of the basic anatomy of the mouth and the tooth similar to what may be generated using the illustrative i-CAT imaging system described above. This visual aid has many limitations, namely, the multiple objects in the image have not been identified. Additionally, the image is essentially a raw image that has not been standardized using some type of calibrated sample. - Referring to
FIG. 3 there is shown an illustrative flowchart of a method for visualizing objects in a 3-D image 70. Theillustrative method 70 for visualizing a 3-D medical image comprises receiving a plurality of high resolution 3-D medical data atblock 72 that are generated using a tomography technique, e.g. computed tomography (CT) scans. By way of example and not of limitation, the method for visualizing a dental image comprises receiving a plurality of high resolution 3-D dental data associated with a patient's mouth that is generated using x-ray tomography. - The high resolution 3-D data received at
block 72 includes a standard for calibration purposes. For example, with respect to CT scans, the standard may have a particular density that can be associated with a bone density. The standard is composed of a material that can be associated with the bone density of an illustrative tooth. The standard may placed adjacent to the patient and held physically or mechanically in place. Alternatively, the patient may place the standard in the mouth and bite the standard. - The method then proceeds to block 74 where the high resolution data is converted into an image comprised of a plurality of cubic voxels. At
block 76, the method proceeds to identify the location for each cubic voxel. The method then proceeds to identify a degree of attenuation for the voxels atblock 78. Attenuation is the reduction in amplitude and intensity of a signal. - At
block 80, the method associates a common density with the degree of attenuation. The method also associates the degree of attenuation for the standard with the previously determined standard density related to block 72. For example, the degree of attenuation for each voxel is associated with at least one of a plurality of common bone densities. - The method then proceeds to block 82 that identifies an object by combining the voxels having the common density and determines an initial shape for the object. By way of example and not of limitation, the identifying of the object may comprise comparing the initial shape of the object to a standard shape. The common density may also be modified by a user, thereby resulting in the object having a different shape. For example, the 3-D data may be dental data and the common density is a bone density associated with teeth, mouth, or jaw. By way of example and not of limitation, the method then proceeds to generate dental objects by combining voxels having one of the common bone densities. The boundaries for each dental object are also determined. Various opportunities may be presented where each dental object is compared to a standard shape to confirm identification of each dental object.
- The method then proceeds to block 84 where at least one object is then tagged for
further analysis 78. For example, the tagged objects in the tooth may be associated with a “metatag” so that each object can be quickly identified and viewed. A “metatag” as used herein refers to a “tag” that is associated with each object, wherein the “tag” is searchable and is used to provide a structured means for identifying object so that the “tagged” object or objects can be viewed. The tagged object may then be extracted for further analysis. The extracted object may then be viewed using a plurality of different perspectives. The tagged and extracted object can then be viewed by slicing the object at desired locations. An illustrative object may be a particular tooth object, an enamel object, a dentin object, a pulp object, a root object, a nerve object, or any other such dental object associated with mouth and jaw. The plurality of objects may also be identified such as teeth objects, a plurality of nerve objects, and a plurality of bone objects. More generally, a plurality of objects may also be identified by combining the voxels having one of a plurality of different common densities and by determining the boundaries for each object. - At
block 86, each of the objects is then compared to a standard shape, and each of the objects is then tagged to permit one or more objects to be combined. The plurality of objects may also be identified such as teeth objects, a plurality of nerve objects, a plurality of bone objects, or any other such object. - The flowchart also describes modifying the shape of at least one object in a scanned 3-D medical image at
block 88. After the method proceeds to tag a first object and a second object, a common boundary between the first object and the second object is identified. The common boundary is configured to identify a change in bone density between the first object and the second object. The method permits a user to modify the common boundary by permitting the user to modify the apparent bone density of the first object. The method also provides for coloring each voxel according to each of the bone densities. - The common boundary spans a relatively broad area when there is little change in bone density between the first object and the second object. The method also permits evaluating a plurality of standard shapes when generating the first object and the second object. For example, each of the plurality of objects may have a plurality of tags, in which each tag may be extracted from the image as represented by
block 90. By way of example and not of limitation, the first object is tagged as a first tooth and the second object is a second tooth. In another illustrative example, the first object and said second object is selected from a group consisting of a tooth object, an enamel object, a dentin object, a pulp object, a root object, a nerve object, a plurality of teeth objects, a plurality of nerve objects, or a plurality of bone objects. Additionally, themethod 70 also supports performing imaging operation such as slicing objects as represented byblock 92 and described in further detail below. - In operation, the method involves known physiologies discovered by the method above and supports analyzing relevant materials. After a 3-D DICOM file is converted to 3-D volumetric image, if the process has not already been completed, the volume is oriented according to axes, body portion contained, scale, etc. Object identification may be performed as a function of common densities, density transitions, and known or standard shape similarities. A map of the objects can then be created and displayed.
- The systems and method described may be applied to non-specific objects, issue identification by density, adjacent material, and general location. Margin (junction) shape determination, specific material shape (object) determination based on material profile, and cataloging of same may also be performed. For example, the identification of objects, passageways, etc. (e.g. teeth, nerve canals, implants, vertebrae, jaw, etc.) is performed. The objective is to identify recurring examples of similar objects such as teeth, and to catalog their identification, both by normative standards and by reference to statistically compiled identifiers and shape.
- Referring to
FIG. 4A there is shown anillustrative drawing 100 with dental and jaw object identification. As presented, the tagged objects in the tooth are associated with a “metatag” or “searchable tag” so that each object can be quickly identified and viewed. A variety of soft tissues objects such as nerve objects are shown. The nerve objects refer to sensitive tissue in the pulp of a tooth, or any bundle of nerve fibers running to various organs in the body. Additionally, a standard 102 for calibration purposes is shown. By way of example and not of limitation, these nerve objects are typically identified using MRI or CT scans. - Referring to
FIG. 4B there is shown an exploded view of a thirdmolar tooth object 110, which is identified using the systems and methods described herein. The tooth is a set of hard, bone-like structures rooted in sockets in the jaws of vertebrates, typically composed of a core of soft pulp surrounded by a layer of hard dentin that is coated with cementum or enamel at the crown and used for biting or chewing foods or as a means of attack or defense. The tooth object is composed of a variety of different objects. One such object is an enamel object which is the hard, calcareous substance covering the exposed portion of a tooth. Another object is dentin, which is the main, calcareous part of a tooth, beneath the enamel, and surrounding the pulp chamber and root canals. The pulp object is the soft tissue forming the inner structure of a tooth and containing nerves and blood vessels. The root object is the embedded part of an organ or structure such as a tooth, or nerve, and includes the part of the tooth that is embedded in the jaw and serves as support. The root canal objects refers to the portion of the pulp cavity inside the root of the tooth, namely, the chamber within the root of the tooth that contains the pulp. The Gingiva or Gum object is the firm connective tissue covered by mucous membrane that envelops the alveolar arches of the jaw and surrounds the neck of the teeth. The neck object is the constriction between the root and the crown and can also be referred to as the Cemental-Enamel-Junction. - Referring to
FIG. 5 there is shown an illustrative 3-D image of an illustrative tooth object 120. The tooth object 120 comprises each of the objects described above such as the enamel, dentin and pulp. A variety of different slices of the 3-D image are presented. For exampleFIG. 6 provides an illustrativefirst slice 122 of the tooth object inFIG. 5 .FIG. 7 provides an illustrativesecond slice 124 of the tooth object inFIG. 5 , andFIG. 8 is an illustrativethird slice 126 of the tooth object. Each of these drawings depict that the tagged objects can be “sliced” to provide a clearer view of the particular tooth. This slicing process may also be used for anomaly detection as described below. - Referring to
FIG. 9 there is shown an illustrative flowchart for anomaly detection and for modeling growth rates that is a continuation of the flowchart inFIG. 3 . An anomaly is a deviation or departure from a normal, or common order, or form, or rule, and is generally used to refer to a substantial defect. An “irregularity” is distinguishable from an anomaly since “irregular” simply means lacking symmetry, evenness, or having a minor defect. The flowchart describes a method for identifying anomalies in a scanned 3-D dental image. The method accesses a database 206 (shown in FIG. IA) having a plurality of data fields related to location for a plurality of teeth, a plurality of locations for each section of tooth, a plurality of standard shapes associated with the teeth, a plurality of standard shapes associated with each of the sections of tooth, and a plurality of bone density data for each section of tooth. - As previously described in
FIG. 3 , a 3-D image having a plurality of cubic voxels is generated, and the location for each voxel is identified. For the illustrative example described herein, the method then proceeds to identify a signal strength for each cubic voxel, and associates the signal strength for each voxel with the bone density data. Signal strength refers to the total amount of power of RF received by the receiver. This is divided into useful signal, referred to as EC/IO, and the noise floor. - The method at
block 92 performs object identification atblock 132 where an illustrative first object is generated by combining a first grouping of voxels having a first bone density. Atblock 134, the illustrative first object is compared to objects in the database 206 (shown in FIG. IA). The method then proceeds to identify irregularities atblock 136. Atblock 138, anomalies are identified after comparing the first object to one or more fields in thedatabase 206. The database comprises a plurality of normative standards and statistical standards for anomaly detection that distinguished between anomalies and irregularities. - The anomaly detection at
block 138 may also comprise generating a plurality of other objects and tagging the objects so that one or more objects may be combined. The method may then proceed to identify one or more anomalies associated with the plurality of objects. For example, the method supports identifying one or more anomalies associated with at least one object that is tagged as a tooth object, in which the tooth object further comprises a plurality of tagged objects selected from a group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object. - The method then proceeds to block 140 and performs the process of mathematically modeling growth rates. Although the illustrative example of teeth is described herein, teeth are not the only objects that grow and it shall be appreciated that the systems and methods described herein may be used to model bone growth and bone decay in general. Growth rate projections may be based on such parameters as age, gender, height, weight, ethnicity, and other such parameters that may be valuable to mathematically modeling growth rates. Those skilled in the art shall appreciate that measurements such as bone growth are also primary indicators and are provided for illustrative purposes only.
- At
block 142, the relational effects resulting from having modeled the growth of a particular object are determined. Thus, the modeled growth results in changes to the local conditions, and these changes are presented to the user. - The method then proceeds to
decision diamond 144 where the determination of whether to change any of the parameters described above is necessary. Therefore, modeled growth rates may be changed, thresholds for anomaly detection may be changed, and the basis for object identification may also be modified. - In operation, at least one expected growth rate is provided for at least one tooth. After the first tooth object is compared to the first tooth object data in the database, the method then proceeds to mathematically model a growth rate for the first tooth object using the expected growth rate, and modifies the location of a plurality of objects surrounding the first tooth object due to the growth of the first tooth object. The method may then proceed to identify an anomaly after comparing the first object to one or more fields in the database. Objects may then be tagged so that so that one or more objects may be combined. Anomalies may then be associated with one or more tooth objects selected from a group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object.
- By way of example and not of limitation, anomaly detection may be performed by identifying at least one threshold for anomaly detection. The gathered data is then compared to the threshold to determine if one or more anomalies have been detected.
- The potential anomaly may also be associated with a first mathematical model, which is then compared to a second “normative” mathematical model using recently extracted data. The first mathematical model may have variables that can be modified, which mirrors the ability to modify the object. The correlation between the first mathematical model and second mathematical model is determined by a correlation estimate that may be based on the concordances of randomly sampled pairs.
- Additionally, the method may also provide for the use of clustering analysis. Clustering provides an additional method for analyzing the data. Spatial cluster detection has two objectives, namely, to identify the locations, shapes and sizes of potentially anomalous spatial regions, and to determine whether each of these potential clusters is more likely to a valid cluster or simply a chance cluster. The process of spatial cluster detection can separated into two parts: first, determining the expected result, secondly, determining which regions deviate from the expected result.
- The process of determining which regions deviate from the expected result can be performed using a variety of techniques. For example, simple statistics can be used to determine a number of spatial standard deviations, and anomalies simply fall outside the standard deviations. Alternatively, spatial scan statistics can be used as described by Kulldorff. (M. Kulldorff. A Spatial Scan Statistic. Communications in Statistics: Theory and Methods 26(6), 1481-1496, 1997.) In this method, a given set of spatial regions are searched and regions are found using hypothesis testing. A generalized spatial scan framework can also be used. (M. R. Sabhnani, D. B. Neill, A. W. Moore, F.-C. tsui, M. M. Wagner, and J. U. Espino. Detecting anomalous patterns in pharmacy retail data. KDD Workshop on Data Mining Methods for Anomaly Detection, 2005.)
- It shall be appreciated by those skilled in the art that the particular algorithm that is used for anomaly detection will depend on the particular application and be subject to system limitations. Thus, a variety of different algorithms for anomaly detection may be used.
- An illustrative method for anomaly detection for a tooth object is shown in
FIG. 10 throughFIG. 14 . The anomaly detection also includes modeling the crown of a tooth object and the effect the tooth object has on surrounding objects. Referring toFIG. 10A and the exploded view inFIG. 10B , there is shown a normal orientation for a wisdom tooth. In this orientation, the wisdom tooth in question has sufficient space so that there will be no horizontal impaction. - With respect to another patient, an
anomaly 150 is detected inFIG. 11A and the exploded view inFIG. 11B . The anomaly reflects that this is a problem tooth, and this anomaly can immediately be brought to the physician's attention using the systems and method described herein. This image also shows the beginning phase of horizontal impaction, and the formation of a cyst. A cyst is an abnormal membranous sac containing a gaseous, liquid, or semisolid substance. Additionally, at location there is shown a dental cavity/caries that are just starting. - Referring to
FIG. 12A and the exploded view inFIG. 12B there is shown an illustrative example of the progression, i.e. growth, of the cyst and cavity/caries after the appropriate growth models have been associated with theparticular tooth 150 and the surrounding teeth. AtFIG. 13A and the exploded view in 13B, there is shown the effect of cyst growth, and the initial stages of tooth decay ontooth 150. Tooth decay is an infectious, transmissible, disease caused by bacteria. The damage done to teeth by this disease is commonly known as cavities. Tooth decay can cause pain and lead to infections in surrounding tissues and tooth loss if not treated properly. The progression of the tooth decay and cyst formation is then shown inFIG. 14A and exploded view inFIG. 14B . - The illustrative systems and methods described above have been developed to assist in visualizing objects, permitting a user to modify objects, anomaly detection for objects, and for modeling growth rates associated with particular objects. It shall be appreciated by those of ordinary skill in the various arts having the benefit of this disclosure that the system and methods described can be applied to many disciplines outside of the field of dentistry. Furthermore, alternate embodiments of the invention which implement the systems in hardware, firmware, or a combination of hardware and software, as well as distributing the modules or the data in a different fashion will be apparent to those skilled in the art. Further still, the illustrative methods described may vary as to order and implemented algorithms.
- Although the description above contains many limitations in the specification, these should not be construed as limiting the scope of the claims but as merely providing illustrations of some of the presently preferred embodiments of this invention. Many other embodiments will be apparent to those of skill in the art upon reviewing the description. Thus, the scope of the invention should be determined by the appended claims, along with the full scope of equivalents to which such claims are entitled.
Claims (20)
1. A system for visualizing a dental image, comprising:
a plurality of high resolution dental data generated using computed tomography;
a plurality of tooth objects selected for each tooth from the dental data from the group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object;
at least one threshold to detect at least one problem tooth;
a processing module that detects at least one problem tooth;
the processing module mathematically models the growth of at least one tooth object; and
the processing module determines the effect the problem tooth has on at least one other tooth object.
2. The system of claim 1 further comprising a database that includes a database that further includes a plurality of data fields that include a plurality of standard shapes associated with each tooth and a plurality of bone density data for each section of tooth.
3. The system of claim 2 wherein the database includes a plurality of normative standards and at least one statistical standard for anomaly detection.
4. The system of claim 2 further comprising a common boundary between at least two tooth objects that is identified by the system.
5. The system of claim 2 further comprising a particular tooth that is identified by the system for further analysis.
6. The system of claim 5 wherein the processing module analyzes the tooth objects for the particular tooth.
7. The system of claim 6 wherein the processing module analyzes the particular tooth object by slicing the tooth object at one or more locations.
8. A system for visualizing a dental image, comprising:
a plurality of high resolution dental data generated using computed tomography;
a plurality of tooth objects selected for each tooth from the dental data from the group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object;
at least one threshold to detect at least one problem tooth;
a processing module that detects at least one problem tooth;
the processing module mathematically models the decay for at least one tooth object; and
the processing module determines the effect the problem tooth has on at least one other tooth object.
9. The system of claim 8 further comprising a database that includes a database that further includes a plurality of data fields that include a plurality of standard shapes associated with each tooth and a plurality of bone density data for each section of tooth.
10. The system of claim 9 wherein the database includes a plurality of normative standards and at least one statistical standard for anomaly detection.
11. The system of claim 9 further comprising a common boundary between at least two tooth objects that is identified by the system.
12. The system of claim 9 further comprising a particular tooth that is identified by the system for further analysis.
13. The system of claim 5 wherein the processing module analyzes the tooth objects for the particular tooth and analyzes the particular tooth object by slicing the tooth object at one or more locations.
14. A method for visualizing a dental image, comprising:
receiving a plurality of high resolution dental data that is generated using computed tomography;
identifying a plurality of tooth objects for each tooth from the dental data, wherein the plurality of tooth objects is selected from the group consisting of an enamel object, a dentin object, a pulp object, a root object, and a nerve object;
providing at least one threshold to detect at least one problem tooth;
detecting the at least one problem tooth;
mathematically modeling at least one of the growth or decay of the at least one tooth object; and
determining the effect the problem tooth has on at least one other tooth object.
15. The method of claim 14 further comprising providing a database that includes a database that further includes a plurality of data fields that include a plurality of standard shapes associated with each tooth and a plurality of bone density data for each section of tooth.
16. The method of claim 15 wherein the database includes a plurality of normative standards and at least one statistical standard for anomaly detection.
17. The method of claim 15 further comprising identifying a common boundary between at least two tooth objects.
18. The method of claim 15 further comprising identifying a particular tooth for further analysis.
19. The method of claim 18 further comprising analyzing the tooth objects for the particular tooth.
20. The method of claim 19 further comprising analyzing the particular tooth object by slicing the tooth object at one or more locations.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/797,321 US20150313559A1 (en) | 2006-08-11 | 2015-07-13 | System and method for detecting a problem tooth |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US83731106P | 2006-08-11 | 2006-08-11 | |
US11/890,533 US9111372B2 (en) | 2006-08-11 | 2007-08-06 | System and method for object identification and anomaly detection |
US14/797,321 US20150313559A1 (en) | 2006-08-11 | 2015-07-13 | System and method for detecting a problem tooth |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/890,533 Continuation US9111372B2 (en) | 2006-08-11 | 2007-08-06 | System and method for object identification and anomaly detection |
Publications (1)
Publication Number | Publication Date |
---|---|
US20150313559A1 true US20150313559A1 (en) | 2015-11-05 |
Family
ID=40641999
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/890,533 Active 2030-05-16 US9111372B2 (en) | 2006-08-11 | 2007-08-06 | System and method for object identification and anomaly detection |
US14/797,321 Abandoned US20150313559A1 (en) | 2006-08-11 | 2015-07-13 | System and method for detecting a problem tooth |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US11/890,533 Active 2030-05-16 US9111372B2 (en) | 2006-08-11 | 2007-08-06 | System and method for object identification and anomaly detection |
Country Status (1)
Country | Link |
---|---|
US (2) | US9111372B2 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2020123167A1 (en) * | 2018-12-14 | 2020-06-18 | Colgate-Palmolive Company | System and method for oral health monitoring using electrical impedance tomography |
US11207161B2 (en) | 2016-05-30 | 2021-12-28 | 3Shape A/S | Predicting the development of a dental condition |
Families Citing this family (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20090204338A1 (en) * | 2008-02-13 | 2009-08-13 | Nordic Bioscience A/S | Method of deriving a quantitative measure of the instability of calcific deposits of a blood vessel |
US20100253773A1 (en) * | 2008-05-13 | 2010-10-07 | Oota Sadafumi | Intra-oral measurement device and intra-oral measurement system |
GB201002778D0 (en) * | 2010-02-18 | 2010-04-07 | Materialise Dental Nv | 3D digital endodontics |
US9779504B1 (en) | 2011-12-14 | 2017-10-03 | Atti International Services Company, Inc. | Method and system for identifying anomalies in medical images especially those including one of a pair of symmetric body parts |
US9401021B1 (en) | 2011-12-14 | 2016-07-26 | Atti International Services Company, Inc. | Method and system for identifying anomalies in medical images especially those including body parts having symmetrical properties |
US8724871B1 (en) | 2011-12-14 | 2014-05-13 | Atti International Services Company, Inc. | Method and system for identifying anomalies in medical images |
US9135498B2 (en) | 2012-12-14 | 2015-09-15 | Ormco Corporation | Integration of intra-oral imagery and volumetric imagery |
GB2548149A (en) * | 2016-03-10 | 2017-09-13 | Moog Bv | Model generation for dental simulation |
IT201700017965A1 (en) * | 2017-02-17 | 2018-08-17 | Silvio Franco Emanuelli | METHOD AND SIMULATION SYSTEM OF SITE IMPLANT TO OPTIMIZED |
EP3582717B1 (en) | 2017-02-17 | 2023-08-09 | Silvio Franco Emanuelli | System and method for monitoring optimal dental implants coupleable with an optimized implant site |
KR102581685B1 (en) * | 2017-06-30 | 2023-09-25 | 프로메이톤 홀딩 비.브이. | Classification and 3D modeling of 3D oral and maxillofacial structures using deep learning |
CA3100495A1 (en) | 2018-05-16 | 2019-11-21 | Benevis Informatics, Llc | Systems and methods for review of computer-aided detection of pathology in images |
US11367192B2 (en) * | 2019-03-08 | 2022-06-21 | Align Technology, Inc. | Foreign object filtering for intraoral scanning |
EP3967235A4 (en) | 2019-03-25 | 2022-11-30 | Bonewise Inc. | Apparatus and method for assessing bone age of tooth, and recording medium recording instructions for assessing bone age of tooth |
JP7245577B1 (en) * | 2022-06-23 | 2023-03-24 | Rightly株式会社 | Computer system, three-dimensional intraoral data coloring method and program |
CN115688461B (en) * | 2022-11-11 | 2023-06-09 | 四川大学 | Device and method for evaluating degree of abnormality of dental arch and alveolar bone arches based on clustering |
Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020178032A1 (en) * | 2001-05-03 | 2002-11-28 | Benn Douglas K. | Method and system for recording carious lesions |
US7826646B2 (en) * | 2000-08-16 | 2010-11-02 | Align Technology, Inc. | Systems and methods for removing gingiva from computer tooth models |
Family Cites Families (15)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20020176619A1 (en) | 1998-06-29 | 2002-11-28 | Love Patrick B. | Systems and methods for analyzing two-dimensional images |
US7234937B2 (en) | 1999-11-30 | 2007-06-26 | Orametrix, Inc. | Unified workstation for virtual craniofacial diagnosis, treatment planning and therapeutics |
US6944262B2 (en) * | 1999-12-01 | 2005-09-13 | Massie Ronald E | Dental and orthopedic densitometry modeling system and method |
US8073101B2 (en) * | 1999-12-01 | 2011-12-06 | Massie Ronald E | Digital modality modeling for medical and dental applications |
US7373286B2 (en) | 2000-02-17 | 2008-05-13 | Align Technology, Inc. | Efficient data representation of teeth model |
US7717708B2 (en) | 2001-04-13 | 2010-05-18 | Orametrix, Inc. | Method and system for integrated orthodontic treatment planning using unified workstation |
US8021147B2 (en) | 2001-04-13 | 2011-09-20 | Orametrix, Inc. | Method and system for comprehensive evaluation of orthodontic care using unified workstation |
US7156655B2 (en) | 2001-04-13 | 2007-01-02 | Orametrix, Inc. | Method and system for comprehensive evaluation of orthodontic treatment using unified workstation |
JP2002352563A (en) * | 2001-05-25 | 2002-12-06 | Toshiba Corp | Recording device, data management system and data management method |
US7474932B2 (en) | 2003-10-23 | 2009-01-06 | Technest Holdings, Inc. | Dental computer-aided design (CAD) methods and systems |
US7636461B2 (en) * | 2004-02-05 | 2009-12-22 | Koninklijke Philips Electronics N.V. | Image-wide artifacts reduction caused by high attenuating objects in ct deploying voxel tissue class |
US7536461B2 (en) * | 2005-07-21 | 2009-05-19 | International Business Machines Corporation | Server resource allocation based on averaged server utilization and server power management |
US8417010B1 (en) * | 2006-01-12 | 2013-04-09 | Diagnoscan, LLC | Digital x-ray diagnosis and evaluation of dental disease |
US7912257B2 (en) | 2006-01-20 | 2011-03-22 | 3M Innovative Properties Company | Real time display of acquired 3D dental data |
US20090061381A1 (en) | 2007-09-05 | 2009-03-05 | Duane Milford Durbin | Systems and methods for 3D previewing |
-
2007
- 2007-08-06 US US11/890,533 patent/US9111372B2/en active Active
-
2015
- 2015-07-13 US US14/797,321 patent/US20150313559A1/en not_active Abandoned
Patent Citations (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7826646B2 (en) * | 2000-08-16 | 2010-11-02 | Align Technology, Inc. | Systems and methods for removing gingiva from computer tooth models |
US20020178032A1 (en) * | 2001-05-03 | 2002-11-28 | Benn Douglas K. | Method and system for recording carious lesions |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11207161B2 (en) | 2016-05-30 | 2021-12-28 | 3Shape A/S | Predicting the development of a dental condition |
US11918438B2 (en) | 2016-05-30 | 2024-03-05 | 3Shape A/S | Predicting the development of a dental condition |
WO2020123167A1 (en) * | 2018-12-14 | 2020-06-18 | Colgate-Palmolive Company | System and method for oral health monitoring using electrical impedance tomography |
AU2019397263B2 (en) * | 2018-12-14 | 2022-07-07 | Colgate-Palmolive Company | System and method for oral health monitoring using electrical impedance tomography |
Also Published As
Publication number | Publication date |
---|---|
US20090129639A1 (en) | 2009-05-21 |
US9111372B2 (en) | 2015-08-18 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US9111372B2 (en) | System and method for object identification and anomaly detection | |
US10629309B2 (en) | Method and system for 3D root canal treatment planning | |
Agrawal et al. | Artificial intelligence in dentistry: past, present, and future | |
Torres et al. | Characterization of mandibular molar root and canal morphology using cone beam computed tomography and its variability in Belgian and Chilean population samples | |
Grauer et al. | Working with DICOM craniofacial images | |
Saghiri et al. | A new approach for locating the minor apical foramen using an artificial neural network | |
CN113223010B (en) | Method and system for multi-tissue full-automatic segmentation of oral cavity image | |
Porto et al. | Evaluation of volumetric changes of teeth in a Brazilian population by using cone beam computed tomography | |
JP5696146B2 (en) | Method for determining at least one segmentation parameter or optimal threshold of volumetric image data of a maxillofacial object, and method for digitizing a maxillofacial object using the same | |
DE102015122604B4 (en) | Apparatus for assisting diagnosis of periodontal disease, system for assisting diagnosis of periodontal disease, program for assisting diagnosis of periodontal disease, and method for assisting diagnosis of periodontal disease | |
Kim et al. | Automatic extraction of inferior alveolar nerve canal using feature-enhancing panoramic volume rendering | |
Roden-Johnson et al. | Comparison of hand-traced and computerized cephalograms: landmark identification, measurement, and superimposition accuracy | |
Stoetzer et al. | Advances in assessing the volume of odontogenic cysts and tumors in the mandible: a retrospective clinical trial | |
Benyó | Identification of dental root canals and their medial line from micro-CT and cone-beam CT records | |
Robles et al. | A step-by-step method for producing 3D crania models from CT data | |
Kanuri et al. | Trainable WEKA (Waikato Environment for Knowledge Analysis) segmentation tool: machine-learning-enabled segmentation on features of panoramic radiographs | |
Linney et al. | Three-dimensional visualization of computerized tomography and laser scan data for the simulation of maxillo-facial surgery | |
Iurino et al. | Medical CT scanning and the study of hidden oral pathologies in fossil carnivores | |
Zhu et al. | An algorithm for automatically extracting dental arch curve | |
Khatri et al. | Unfolding the mysterious path of forensic facial reconstruction: Review of different imaging modalities | |
Alzaid et al. | Revolutionizing Dental Care: A Comprehensive Review of Artificial Intelligence Applications Among Various Dental Specialties | |
US11576631B1 (en) | System and method for generating a virtual mathematical model of the dental (stomatognathic) system | |
Agarwal et al. | A review paper on diagnosis of approximal and occlusal dental caries using digital processing of medical images | |
Jatti et al. | Image processing and parameter extraction of digital panoramic dental X-rays with ImageJ | |
de Oliveira Lisboa et al. | Three-dimensionally rendering of the sphenoid bone of adolescents using Materialise’s Interactive Medical Image Control System software |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: VISIONARY TECHNOLOGIES, INC., CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ORTEGA, MIREYA;DAUGHERTY, ROGER;SIGNING DATES FROM 20071228 TO 20071230;REEL/FRAME:036066/0852 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |