Method of measureing tooth structure and system thereof

Abstract

Provided is a method for measuring tooth structure, including providing at least one image of a tooth in a longitudinal section; defining an enamel, a dentin, and a cavity in the at least one image; defining first and second protrusions located on an upper surface of the dentin; defining two side ends positioned opposite to each other at the cavity; measuring a thickness of the enamel and the measuring range being from the first protrusion to the second protrusion to obtain an enamel thickness curve; and measuring the distance between the dentin and the cavity, and the measuring range being between the two side ends of the cavity to obtain a dentin thickness curve. A system for measuring tooth structure is also provided to quickly identify tooth structure and accurately assess caries.

Description

Technical Field

[0001] This disclosure relates to a measurement method and system thereof, and more particularly to a method and system for measuring tooth structure. Prior Technology

[0002] In recent years, the development of artificial intelligence (AI) in the dental field has been widely reported. These studies include the development of AI-driven language, the application of AI imaging in diagnosis, and the generation of AI-designed dental crowns. These findings demonstrate the increasing adoption of computational aids in medicine and dentistry. Therefore, the application of AI has become an important trend in dental technology. However, given the complexity of assessing caries-related depths and parameters, existing techniques still need improvement. Summary of the Invention

[0003] One embodiment of this disclosure provides a method for measuring tooth structure, comprising: providing at least one image of a tooth in a longitudinal section; defining enamel, dentin, and cavities in the at least one image; defining a first protrusion and a second protrusion located on the upper surface of the dentin; defining two opposing ends located at the cavity; measuring a plurality of first distances of a plurality of first straight lines between the upper surface of the enamel and the upper surface of the dentin, wherein each of the first straight lines is parallel to the other, and the range of the measurement of the first distances is from the first protrusion to the second protrusion on the upper surface of the dentin, thereby obtaining an enamel thickness curve; and measuring a plurality of second distances of a plurality of second straight lines between the upper surface of the dentin and the upper surface of the cavity, wherein each of the first straight lines and each of the second straight lines is parallel to the other, and the range of the measurement of the second distances is between the two ends of the cavity, thereby obtaining a dentin thickness curve, wherein each of the first straight lines and each of the second straight lines are parallel to the other.

[0004] In some implementations, at least one image includes a ray image.

[0005] In some implementations, the radiographic images include X-ray images, computed tomography images, magnetic resonance imaging (MRI) images, or combinations thereof.

[0006] In some implementations, the cavity includes a pulp chamber, a decayed area, or a combination thereof.

[0007] In some implementations, the step of defining at least one image as enamel, dentin, and cavity includes defining the at least one image as enamel, dentin, and cavity using an image segmentation algorithm.

[0008] In some implementations, the image segmentation algorithm includes semantic segmentation algorithms, instance segmentation algorithms, panoptic segmentation algorithms, or combinations thereof.

[0009] In some implementations, the step of defining the first protrusion and the second protrusion located on the upper surface of the dentin includes defining the first protrusion and the second protrusion located on the upper surface of the dentin using a peak-finding algorithm.

[0010] In some implementations, peak-finding algorithms include Monte Carlo algorithms, direct comparison algorithms, derivative algorithms, quadratic interpolation numerical differentiation algorithms, three-point peak-finding algorithms, or combinations thereof.

[0011] In some implementations, the step of defining these opposite sides located at the cavity includes defining these opposite sides located at the cavity using an edge detection algorithm.

[0012] In some implementations, the edge detection algorithm includes the Canny algorithm, the Laplacian algorithm, the Sobel algorithm, or a combination thereof.

[0013] In some embodiments, at least one image comprises multiple images, wherein multiple enamel thickness curves are obtained in the step of measuring enamel, and multiple dentin thickness curves are obtained in the step of measuring dentin.

[0014] Another embodiment of this disclosure provides a system for measuring tooth structure, comprising: a radiographic device, a processor, and memory. The radiographic device is configured to capture at least one image of a tooth in a longitudinal cross-section. The processor is communicatively connected to the radiographic device. The memory is configured to store a plurality of computer program instructions, which, when executed by a processor, cause the processor to perform the following steps: defining at least one image to delineate enamel, dentin, and a cavity; defining a first protrusion and a second protrusion on the upper surface of the dentin; defining two opposing lateral ends on the cavity; measuring a plurality of first distances of a plurality of first straight lines between the upper surface of the enamel and the upper surface of the dentin, each of the first straight lines being parallel to each other, and the measurement of these first distances extending from the first protrusion to the second protrusion on the upper surface of the dentin, to obtain an enamel thickness curve; and measuring a plurality of second distances of a plurality of second straight lines between the upper surface of the dentin and the upper surface of the cavity, each of the second straight lines being parallel to each other, and the measurement of these second distances extending between the two lateral ends of the cavity, to obtain a dentin thickness curve, wherein each of the first straight lines and each of the second straight lines are parallel to each other.

[0015] In some implementations, at least one image includes a ray image.

[0016] In some implementations, the radiographic images include X-ray images, computed tomography images, magnetic resonance imaging (MRI) images, or combinations thereof.

[0017] In some implementations, the cavity includes a pulp chamber, a decayed area, or a combination thereof.

[0018] In some implementations, the step of defining at least one image as enamel, dentin, and cavity includes defining the at least one image as enamel, dentin, and cavity using an image segmentation algorithm.

[0019] In some implementations, the image segmentation algorithm includes semantic segmentation algorithms, instance segmentation algorithms, panoptic segmentation algorithms, or combinations thereof.

[0020] In some implementations, the step of defining the first protrusion and the second protrusion located on the upper surface of the dentin includes defining the first protrusion and the second protrusion located on the upper surface of the dentin using a peak-finding algorithm.

[0021] In some implementations, the step of defining these opposite sides located at the cavity includes defining these opposite sides located at the cavity using an edge detection algorithm.

[0022] In some embodiments, at least one image comprises multiple images, wherein multiple enamel thickness curves are obtained in the step of measuring enamel, and multiple dentin thickness curves are obtained in the step of measuring dentin. Simple Explanation of the Diagram

[0023] The various aspects of this disclosure will be most readily understood when read in conjunction with the accompanying drawings. It should be noted that, according to industry standard operating procedures, the various features may not be drawn to scale. In fact, for clarity of explanation, the dimensions of the various features can be arbitrarily increased or decreased. To make the above and other objects, features, advantages, and embodiments of this disclosure more apparent and understandable, the accompanying drawings are described below: Figure 1 illustrates a schematic diagram of a system for measuring tooth structure according to one embodiment of this disclosure. Figure 2 illustrates a flowchart of a method for measuring tooth structure according to one embodiment of this disclosure. Figure 3A is a cross-sectional CT image of one embodiment of this disclosure, showing the definition of multiple protrusions and two opposite sides. Figure 3B is a semantically segmented cross-sectional CT image of the enamel, dentin, and pulp cavity of one embodiment disclosed in Figure 3A. Image 3C is a cross-sectional CT image of Image 3B, with computer-based automatic parameter evaluation performed in 10-pixel increments. The 3D figure is a curve of the enamel thickness on the X-axis of the 3C figure, with each pixel corresponding to a distance of 0.02 millimeters (mm). Figure 3E is a curve of the distance from the enamel to the pulp cavity on the X-axis, as shown in Figure 3C. Figure 4A shows cross-sectional CT images of a tooth at different depths along the Y-axis of one embodiment of this disclosure. Figure 4B is a bar chart showing the thickness of multiple enamel layers in Figure 4A, with each pixel corresponding to a distance of 0.02 millimeters. Figure 4C is a bar chart showing the distances from multiple enamel layers to the pulp cavity in Figure 4A, with each pixel corresponding to a distance of 0.02 millimeters. Implementation

[0024] To make the description of this disclosure more detailed and complete, illustrative descriptions of embodiments and specific examples of this disclosure are provided below. However, this is not the only form of implementing or utilizing the specific examples of this disclosure. The embodiments disclosed below can be combined or substituted with each other where advantageous, and other embodiments can be added to one embodiment without further description or explanation. In the following description, many specific details will be set forth in detail to enable the reader to fully understand the following embodiments. However, the embodiments of this disclosure can also be practiced without such specific details.

[0025] In addition, spatial relative terms, such as "down" and "up," are used to conveniently describe the relative relationship of one element or feature to other elements or features in a diagram. These spatial relative terms are intended to encompass different orientations of the device during use or operation, in addition to those shown in the diagram. The device may be positioned otherwise (e.g., rotated 90 degrees or otherwise), and the spatial relative descriptions used herein can be interpreted accordingly.

[0026] In this document, unless otherwise specified in the text, the words “a” and “the” may refer to one or more. It will be further understood that the words “comprising,” “including,” “having,” and similar terms used herein specify the features, regions, integers, steps, operations, elements, and / or components described herein, but do not exclude one or more other features, regions, integers, steps, operations, elements, components, and / or groups thereof described or additionally described herein.

[0027] Furthermore, when numbers or ranges of numbers are described using terms such as “about,” “approximately,” and the like, the term is intended to cover numbers within a reasonable range of variations that, as understood by one skilled in the art, are inherent to the manufacturing process. For example, based on known manufacturing tolerances associated with manufacturing characteristics (having the characteristics associated with the numbers), numbers or ranges of numbers encompass a reasonable range including the described numbers, such as within + / –10% of the described numbers. Moreover, references to numbers and / or letters may be repeated in various instances in this disclosure. This repetition is for simplicity and clarity and does not in itself indicate a relationship between the various embodiments and / or configurations discussed.

[0028] Given the complexity of assessing caries-related depths and parameters, this disclosure provides a method and system for measuring tooth structure, based on clinical definitions and combined with artificial intelligence algorithms, to help clinicians assess caries risk.

[0029] In this article, "longitudinal section" refers to a cross-section cut from the top (enamel) of the tooth towards the bottom (root). Since teeth are three-dimensional structures, any cross-section in which the enamel, dentin, and pulp chamber structures can be observed can be included in the definition of a longitudinal section. For example, when the tooth's central axis (e.g., the Z-axis) is growing normally and approximately perpendicular to a horizontal plane (e.g., the XY plane), the longitudinal section can be an XZ section, a YZ section facing that tooth, or any section rotated 360 degrees around the Z-axis. Similarly, when the tooth is tilted (e.g., the tooth's central axis is tilted at an angle to the Z-axis), the longitudinal section can also be an XZ section, a YZ section facing that tooth, or any section rotated 360 degrees around the Z-axis, as long as the enamel, dentin, and pulp chamber structures can be observed.

[0030] The following examples and experimental cases illustrate the method and system for measuring tooth structure disclosed herein in more detail. However, they are only for illustrative purposes and are not intended to limit the scope of the disclosure. The scope of protection of the disclosure shall be defined by the appended claims.

[0031] Although the methods disclosed herein are illustrated using a series of operations or steps, the order in which these operations or steps are shown should not be construed as a limitation of this disclosure. For example, some operations or steps may be performed in a different order and / or simultaneously with other steps. Furthermore, it is not necessary to perform all illustrated operations, steps, and / or features to achieve the implementation of this disclosure. In addition, each operation or step described herein may comprise several sub-steps or actions.

[0032] For clarity, features and elements that are known in the domain and are not essential for understanding the principles described will be omitted.

[0033] Example

[0034] Figure 1 is a schematic diagram of a system 100 for measuring tooth structure according to some embodiments of the present disclosure. As shown in Figure 1, the system 100 for measuring tooth structure includes a ray device 110, a processor 120, and a memory 130. In some embodiments, the system 100 for measuring tooth structure may include a communication chip (not shown in the figure).

[0035] X-ray device 110 is configured to capture at least one image of a tooth in a longitudinal section. In some embodiments, X-ray device 110 includes, but is not limited to, an X-ray machine, a computed tomography scanner, a nuclear magnetic resonance scanner, etc. In some embodiments, the computed tomography scanner includes a cone-beam computed tomography scanner (CBCT, ProMax 3D Plus, Planmeca, Finland) to acquire a series of longitudinal sectional images of a single tooth, capturing slices from the buccal to the lingual side. The spatial tissue structure of each longitudinal sectional layer of the tooth is analyzed.

[0036] Processor 120 is connected to X-ray device 110. In some embodiments, processor 120 may be implemented as an integrated circuit such as a microcontroller, microprocessor, digital signal processor, application-specific integrated circuit (ASIC), logic circuit, or other similar elements or combinations thereof. The communication chip may be implemented as a global system for mobile communication (GSM), personal handyphone system (PHS), long term evolution system (LTE), worldwide interoperability for microwave access (WiMAX), wireless fidelity system (Wi-Fi), or Bluetooth transmission, etc. In some embodiments, processor 120 may be located in a smart electronic device, including but not limited to computers, mobile phones, tablets, etc.

[0037] Memory 130 is configured to store a plurality of computer program instructions that are executed by processor 120. In some embodiments, memory 130 may be any type capable of storing information accessible to processor 120, including computer device readable media or other media storing data readable by electronic devices, such as hard disks, memory cards, ROM, RAM, or combinations thereof, and other writable and read-only memory. In some embodiments, computer program instructions may be any set of instructions that are executed directly or indirectly by processor 120. For example, computer program instructions may be stored as computer device code on a computer device readable medium. In this regard, the terms "instruction" and "program" are used interchangeably herein. Instructions may be stored in object code format for direct processing by processor 120, or in any other computing device language, including scripts or sets of standalone source code modules that are interpreted on demand or compiled in advance.

[0038] To provide a detailed description of how the system 100 for measuring tooth structure operates, the following explanation will be accompanied by Figure 2. It should be understood that, unless otherwise specified, the order of the steps mentioned in Figure 2 can be adjusted as needed, and they can be performed simultaneously or partially simultaneously, or even additional steps can be added or some steps can be omitted.

[0039] Figure 2 illustrates a flowchart of a method 200 for measuring tooth structure according to some embodiments of the present disclosure. In step S210, at least one image of the tooth in a longitudinal section is provided. Next, in step S220, the at least one image defines the enamel, dentin, and cavities. Next, in step S230, a first protrusion and a second protrusion located on the upper surface of the dentin are defined. Next, in step S240, two opposing lateral ends located at the cavities are defined. Next, in step S250, a plurality of first distances are measured between a plurality of first straight lines parallel to each other, and the range of these first distances is from the first protrusion to the second protrusion on the upper surface of the dentin, to obtain an enamel thickness curve. Next, in step S260, a plurality of second distances are measured between the upper surface of the dentin and the upper surface of the cavity, with each of these second lines being parallel to the others, and the range of these second distances being measured is between the two lateral ends of the cavity, thereby obtaining a dentin thickness curve.

[0040] Specifically, in step S210, at least one image of the tooth in a longitudinal section is provided. In some embodiments, at least one image of the tooth in a longitudinal section is captured using a radiographic device, such as an X-ray image captured by an X-ray machine, a computed tomography (CT) scan image captured by a computed tomography (CT) scanner, or an MRI image captured by an MRI scanner. In some embodiments, at least one image includes, but is not limited to, one, two, three, or more images. For example, multiple images of the tooth in a longitudinal section are captured, i.e., multiple longitudinal sections of the tooth at different depths, to facilitate observation of the tooth structure at different depths.

[0041] In step S220, at least one image is defined to delineate enamel, dentin, and cavities. In some embodiments, a cavity includes a pulp chamber, a carious area, or a combination thereof. Since soft tissue within the pulp chamber and cavities caused by caries cannot be specifically visualized in radiographic images, they are presented as cavities. In some embodiments, at least one image is defined to delineate enamel, dentin, and cavities using an image segmentation algorithm. The image segmentation algorithm includes, but is not limited to, semantic segmentation algorithms, instance segmentation algorithms, panoptic segmentation algorithms, or combinations thereof. In some embodiments, the semantic segmentation algorithm includes semantic segmentation algorithms based on convolutional neural networks (CNNs), semantic segmentation algorithms based on recurrent neural networks (RNNs), semantic segmentation algorithms based on generative adversarial networks (GANs), semantic segmentation algorithms based on transformers, or combinations thereof.

[0042] In step S230, a first protrusion and a second protrusion located on the upper surface of the dentin are defined. In some embodiments, a peak-finding algorithm is used to define the first and second protrusions located on the upper surface of the dentin. The peak-finding algorithm uses a numerical array of distances from the tangents of each vertex of a geometric inner product calculation polygon to the horizontal line to locate the peak in the numerical array. In some embodiments, the peak-finding algorithm includes a Monte Carlo algorithm, a direct comparison algorithm, a derivative algorithm, a quadratic interpolation numerical differential algorithm, a three-point peak-finding algorithm, or a combination thereof.

[0043] In step S240, two opposing ends located at the cavity are defined. In some embodiments, the two ends are defined by the left and right edges of the cavity (e.g., the left and right edges on the XZ plane in the image), serving as the basis for subsequent measurement of the dentin thickness range. In some embodiments, these opposing ends located at the cavity are defined using an edge detection algorithm. In some embodiments, the edge detection algorithm includes the Canny algorithm, the Laplacian algorithm, the Sobel algorithm, or a combination thereof.

[0044] In step S250, a plurality of first distances are measured between a plurality of first straight lines between the upper surface of the enamel and the upper surface of the dentin. These first straight lines are parallel to each other, and the measurement range of these first distances extends from a first protrusion to a second protrusion on the upper surface of the dentin, thereby obtaining an enamel thickness curve. In some embodiments, on the XZ plane of the image, these first straight lines are only required to be parallel to each other, and these first distances may or may not be parallel to the Z-axis direction.

[0045] In step S260, a plurality of second distances are measured between a plurality of second straight lines from the upper surface of the dentin to the upper surface of the cavity. These second straight lines are parallel to each other, and the measurement range for these second distances is between the two lateral ends of the cavity, thus obtaining a dentin thickness curve. In some embodiments, on the XZ plane of the image, as long as these second straight lines are parallel to each other, these second distances may or may not be parallel to the Z-axis direction. In some embodiments, the first straight lines are parallel to the second straight lines. That is, the angle at which the first straight lines intersect the Z-axis is the same as the angle at which the second straight lines intersect the Z-axis.

[0046] One embodiment of this disclosure provides a method for measuring tooth structure. First, an image of the tooth in a longitudinal section is captured using a cone-beam computed tomography (Fig. 3A 310 on the XZ plane of the X and Z axes). Next, a semantic segmentation algorithm is used to define regions of interest in the dental radiographs, specifically enamel 321, dentin 322, and pulp chamber 323 (Fig. 3B 320). Then, key parameters for caries assessment are calculated, namely two indicators: (1) enamel thickness (first distance 331) and (2) distance from enamel to pulp chamber (second distance 332) (Fig. 3C 330). The enamel thickness of most enamel regions is calculated. Given that enamel near the gingival region is typically thinner, a peak-finding algorithm is used to detect corner points on the lower surface of the enamel (the upper surface of the dentin), where cutoff points are established to prevent inaccurate measurements. As shown in Figure 310 of 3A, the peak-finding algorithm detects the first protrusion 311 and the second protrusion 312 on the upper surface of the dentin, which are circled in the figure and are also called the external configuration transition point.

[0047] To calculate the distance from enamel 321 to pulp cavity 323, the vertical distance (i.e., the distance in the X-axis direction) from the lower surface of enamel 321 to pulp cavity 323 was measured. Since pulp cavity 323 is relatively small, some areas on the lower surface of enamel 321 are not vertically aligned with it. To address this, an edge detection algorithm identified the left and right boundaries (first side 313 and second side 314) on the contour of pulp cavity 323 and calculated a second distance 332 limited to the area within these boundaries. Through computer-based automatic evaluation, each parameter was evaluated in 10-pixel increments, producing detailed measurement curves (Figure 3D 340 and Figure 3E 350).

[0048] This method comprehensively assesses the enamel thickness (321) (first distance 331) and the distance from dentin to the pulp chamber (second distance 332), allowing for statistical analysis of parameters such as average, maximum, and minimum values ​​to improve the accuracy of caries assessment. Furthermore, this computational assessment method helps evaluate the risk of caries at different depths within cone-beam computed tomography (CBCT) images (Figure 4A, 410, longitudinal sections of the same tooth at three different depths). Through automated calculation, parameters for different layers can be determined, generating a landscape depicting the changes in depth in three-dimensional space (Figures 4B, 420, and 4C, 430). In some embodiments, the second distance 332 is a key value for assessing the severity of caries in the measurement of caries risk at different depths. For example, if the minimum value among multiple second distances 332 drops to zero, it indicates that bacteria have entered the pulp chamber, causing severe caries with pulp necrosis, requiring root canal treatment. Therefore, by comparing the second distance 332 at different depths to obtain the minimum value, the caries risk at different depths of the second distance 332 can be obtained, and then the caries risk of the entire tooth can be summarized. In some embodiments, a higher value of the second distance 332 indicates a lower caries risk, and a lower value of the second distance 332 indicates a higher caries risk. In some embodiments, the specific value of the second distance 332 and its correlation with the degree of caries can be based on the standard definition of the International Caries Detection and Assessment System (ICDAS).

[0049] Due to the complex anatomical structure of natural teeth, 3D spatial imaging can enable more accurate assessments. This disclosure presents a technique that combines image recognition, language processing, and spatial computation. Distance calculations between different tooth structures are based on AI image learning technology. Therefore, the methods and systems disclosed herein can serve as pre-screening aids, enabling clinicians to quickly identify key layers and accurately assess caries.

[0050] Although the present disclosure has been described above with reference to embodiments, it is not intended to limit the present disclosure. Anyone skilled in the art may make various modifications and refinements without departing from the spirit and scope of the present disclosure. Therefore, the scope of protection of the present disclosure shall be determined by the appended claims.

[0051] 100: Systems for measuring tooth structure 110: X-ray equipment 120: Processor 130: Memory 200: Method S210, S220, S230, S240, S250, S260: Steps 310, 320, 330, 340, 350, 410, 420, 430: Figure 311: First protrusion 312: Second protrusion 313: First side end 314: Second side end 321: Enamel 322: Dentin 323: Pulp cavity 331: First distance 332: Second distance X, Y, Z: Axes

[0052] Domestic storage information (please note in order of storage institution, date, and number) none Overseas storage information (please note in the order of storage country, institution, date, and number) none

Claims

1. A method for measuring tooth structure, comprising: providing at least one image of a tooth in a longitudinal section; defining an enamel, a dentin, and a cavity in the at least one image; defining a first protrusion and a second protrusion located on a superior surface of the dentin; defining two opposing lateral ends located at the cavity; measuring a plurality of first distances of a plurality of first straight lines between a superior surface of the enamel and a superior surface of the dentin, wherein each of the first straight lines is parallel to the other, and the measurement range of the first distances extends from the first protrusion to the second protrusion on the superior surface of the dentin, thereby obtaining an enamel thickness curve; and measuring a plurality of second distances of a plurality of second straight lines between a superior surface of the dentin and a superior surface of the cavity, wherein each of the first straight lines is parallel to the other, and the measurement range of the second distances extends between the two lateral ends of the cavity, thereby obtaining a dentin thickness curve, wherein each of the first straight lines is parallel to the other.

2. The method as described in claim 1, wherein the at least one image comprises a ray image.

3. The method as described in claim 2, wherein the radiographic image comprises an X-ray image, a computed tomography image, an magnetic resonance imaging image, or a combination thereof.

4. The method as described in claim 1, wherein the cavity comprises a pulp chamber, a carious area, or a combination thereof.

5. The method as described in claim 1, wherein the step of defining the enamel, dentin, and cavity in the at least one image comprises defining the enamel, dentin, and cavity in the at least one image using an image segmentation algorithm.

6. The method as described in claim 5, wherein the image segmentation algorithm comprises a semantic segmentation algorithm, an instance segmentation algorithm, a panoptic segmentation algorithm, or a combination thereof.

7. The method as claimed in claim 1, wherein the step of defining the first protrusion and the second protrusion located on the upper surface of the dentin includes defining the first protrusion and the second protrusion located on the upper surface of the dentin using a peak-finding algorithm.

8. The method as described in claim 7, wherein the peak finding algorithm includes the Monte Carlo algorithm, the direct comparison algorithm, the derivative algorithm, the quadratic interpolation numerical differentiation algorithm, the three-point peak finding algorithm, or a combination thereof.

9. The method as described in claim 1, wherein the step of defining the opposite sides located at the cavity comprises defining the opposite sides located at the cavity using an edge detection algorithm.

10. The method as described in claim 9, wherein the edge detection algorithm comprises the Canny algorithm, the Laplacian algorithm, the Sobel algorithm, or a combination thereof.

11. The method as claimed in claim 1, wherein the at least one image comprises a plurality of images, wherein the step of measuring the enamel yields a plurality of enamel thickness curves, and wherein the step of measuring the dentin yields a plurality of dentin thickness curves.

12. A system for measuring tooth structure, comprising: a radiographic device configured to capture at least one image of a tooth in a longitudinal section; a processor communicatively connected to the radiographic device; and a memory configured to store a plurality of computer program instructions, which, when executed by the processor, cause the processor to perform the following steps: defining an enamel, a dentin, and a cavity in the at least one image; defining a first protrusion and a second protrusion on a superior surface of the dentin; and defining two opposing lateral ends of the cavity. A plurality of first distances are measured between a plurality of first straight lines parallel to each other, and the range of the first distances is from the first protrusion to the second protrusion on the upper surface of the dentin, to obtain an enamel thickness curve; and a plurality of second distances are measured between a plurality of second straight lines parallel to each other, and the range of the second distances is between the two lateral ends of the cavity, to obtain a dentin thickness curve, wherein each of the first straight lines is parallel to each other.

13. The system as described in claim 12, wherein the at least one image comprises a ray image.

14. The system as described in claim 13, wherein the radiographic image comprises an X-ray image, a computed tomography image, an magnetic resonance imaging image, or a combination thereof.

15. The system as described in claim 12, wherein the cavity comprises a pulp chamber, a carious area, or a combination thereof.

16. The system as claimed in claim 12, wherein the step of defining the at least one image as the enamel, the dentin, and the cavity comprises defining the at least one image as the enamel, the dentin, and the cavity using an image segmentation algorithm.

17. The system as described in claim 16, wherein the image segmentation algorithm comprises a semantic segmentation algorithm, an instance segmentation algorithm, a panoptic segmentation algorithm, or a combination thereof.

18. The system as claimed in claim 12, wherein the step of defining the first protrusion and the second protrusion located on the upper surface of the dentin includes defining the first protrusion and the second protrusion located on the upper surface of the dentin using a peak-finding algorithm.

19. The system as claimed in claim 12, wherein the step of defining the opposite sides located at the cavity comprises defining the opposite sides located at the cavity using an edge detection algorithm.

20. The system as claimed in claim 12, wherein the at least one image comprises a plurality of images, wherein the step of measuring the enamel yields a plurality of enamel thickness curves, and wherein the step of measuring the dentin yields a plurality of dentin thickness curves.

Images