An adaptive dental arch region generation method based on oral CBCT

Abstract

The present invention proposes an adaptive dental arch region generation method based on oral CBCT, comprising acquiring CBCT images from an oral cone beam CT device, obtaining a segmentation threshold of bones and enamel using an adaptive method, locating a lower cavity crown plane using an adaptive method, generating a maximum density projection image, generating a binary image, and generating a dental arch region; the present invention can adapt to scanned images of different CT devices, and processes abnormal extreme values ​​with large deviations in CT images, and has strong versatility and fault tolerance. At the same time, the corresponding dental arch region can be accurately generated according to different situations of whether the upper and lower cavity teeth are occluded, and whether there are missing teeth.

Description

Technical Field

[0001] The invention relates to an adaptive dental arch region generation method based on oral CBCT, and belongs to the field of computer image processing. Background Art

[0002] With the continuous development and progress of oral medical technology, oral cone beam computed tomography (CBCT) is increasingly used in clinical practice, such as pre- and post-surgery analysis of dental implants, orthodontic analysis, and oral disease diagnosis. In the process of oral diagnosis and treatment, doctors need to observe oral panoramic films to obtain more information in order to better observe the oral condition of patients. CBCT images can generate panoramic films through image processing technology, and the generation of panoramic films requires accurate dental arch lines, and the generation of dental arch lines requires accurate dental arch areas.

[0003] Oral medical institutions may have multiple oral cone beam computed tomography devices of different brands and models. The CT value ranges of images collected by different devices are also different. For example, the CT value range of some oral cone beam CT devices is -1000 to 15383, and the range of some devices is 400 to 4095. The CT values ​​of soft tissues, bones, and teeth in different CT value ranges are also different, and the corresponding bone and enamel segmentation thresholds are also different. At the same time, due to different diagnosis and treatment needs, the patient's upper and lower teeth need to be occluded or not, and whether the teeth are occluded or not needs to be handled separately, otherwise it will affect the positioning of the lower crown plane, and then affect the extraction of the dental arch area. The existing dental arch area generation and dental arch curve generation methods can only process images collected by the same cone beam CT device, and there is no separate processing for whether the upper and lower teeth are occluded.

[0004] Therefore, in view of the shortcomings of the existing technology, it is necessary to provide an adaptive dental arch area generation method based on oral CBCT to solve the shortcomings of the existing technology. Summary of the invention

[0005] The purpose of the present invention is to realize that CBCT images generated by different CT devices and different occlusal conditions can adaptively generate corresponding dental arch areas. To achieve the above purpose, the present invention provides the following technical solutions:

[0006] An adaptive dental arch region generation method based on oral CBCT, the specific steps are as follows:

[0007] Step 1: Scanning the oral region based on an oral cone beam CT device to obtain a first CT image and acquire data of the first CT image; wherein the data of the first CT image includes a CT value of each voxel and a cross-sectional slice of the first CT image;

[0008] Step 2: Based on the CT value of each voxel in the first CT image, each CT value and its corresponding quantity are counted, and the abnormal extreme values ​​in the statistical results are first deleted, and then the bone segmentation threshold and the enamel segmentation threshold are respectively obtained by an adaptive method;

[0009] Step 3: crop the first CT image, leaving the facial part including the oral cavity, to obtain a second CT image;

[0010] Step 4: Filter all voxels in the second CT image whose CT values ​​are less than the enamel segmentation threshold, and update their CT values ​​to 0, to obtain a third CT image, and generate corresponding cross-sectional slices of the third CT image;

[0011] Step 5: Sort the cross-sectional slices of the third CT image from bottom to top as 1, 2, ..., m; perform statistics on the sum of the CT values ​​corresponding to each pixel in each cross-sectional slice to generate a cross-sectional CT value sum histogram; then determine the tooth occlusion of the upper and lower cavities by the peak characteristics of the cross-sectional CT value sum histogram, and obtain the lower cavity crown plane and the cross-sectional slice number n where it is located; wherein the abscissa of the cross-sectional CT value sum histogram is the cross-sectional slice number, and the ordinate is the sum of the CT values ​​corresponding to each pixel in the corresponding cross-sectional slice;

[0012] Step 6: For the first CT image, take the cross-sectional slice n where the lower cavity crown plane is located obtained in step 5 as the starting slice, take the cross-sectional slices numbered from n to [n-20%m] as the slice interval, perform maximum density projection on the slices in the slice interval, and obtain a maximum density projection map; wherein the maximum density projection map is a two-dimensional plane map;

[0013] Step 7: binarize the maximum density projection image obtained in step 6 using the bone segmentation threshold to obtain a binary image; wherein the binarization method is: for each pixel point in the maximum density projection image, if the pixel value is less than the bone segmentation threshold, update its pixel value to 0; otherwise, update its pixel value to 1;

[0014] Step 8: Corrode the binary image obtained in step 7 to remove burrs and isolated pixels, and then count the sum of pixel values ​​of each connected area in the binary image, remove the area where the sum of pixel values ​​is less than a preset value, and obtain a new binary image;

[0015] Step 9: Perform a closing operation on the new binary image to fill the small holes; again count the sum of the pixel values ​​of each connected area in the new binary image, retain the connected area with the largest pixel value sum, delete other areas, and obtain the largest connected area;

[0016] Step 10: Fill the holes in the maximum connected area obtained in step 9 to obtain the maximum connected area after hole filling, that is, the dental arch area.

[0017] Furthermore, in the aforementioned step 2, the bone segmentation threshold and the enamel segmentation threshold are obtained through steps 201 to 205:

[0018] Step 201: Count the total number of voxels Total1 of the first CT image, and use the following formula to obtain the number of extreme values ​​Num o :

[0019] Num o =[Total 1* K o ]

[0020] Among them: extreme value coefficient K o is 0.01; the operation [x] means taking the integer part of x;

[0021] Step 202: Based on the CT value of each voxel in the first CT image, all different CT values ​​after deduplication in the first CT image are sorted from small to large to obtain a first CT value sorting: C1, C2, ..., C n ; and count the number of CT values ​​corresponding to V1, V2...V n ;

[0022] Step 203: Delete abnormal extreme values ​​in the first CT image according to different situations:

[0023] If V1 is greater than Num o , then update the number of C1 to V1-Num o ;

[0024] If V1 is equal to Num o , delete C1 and its corresponding quantity, and replace C2, C3, ... n Regenerate the second order of CT values: C1, C2, C3..., the corresponding quantities are V1, V2...V n ;

[0025] If V1 is less than Num o , calculate V1+V2, V1+V2+V3, ..., until V1+V2+...V i The first time greater than Num o , delete C1, C2, ... C i-1 and the corresponding quantity, C i The quantity is updated to V1+V2+...V i -Num o ; C i , C i+1 ……C nRegenerate the second order of CT values: C1, C2, C3..., the corresponding quantities are V1, V2...V n ;

[0026] Step 204: Count the total number of voxels in the first CT image after removing the abnormal extreme value, Total2, and use the following formula to obtain the bone threshold number Num b and enamel threshold number Num t ;

[0027] Num b =[Total 2* K b ]

[0028] Num t =[Total 2* K t ]

[0029] Where: Bone threshold coefficient K b and enamel threshold coefficient K t are all empirical coefficients; operation [x] represents taking the integer part of x; step 205: second sorting based on CT value: C1, C2...C n and the corresponding quantities V1, V2, ... V n , calculate V1+V2, V1+V2+V3, ... in sequence, when V1+V2+...V b The first time greater than Num b , V b The corresponding CT value is recorded as the bone segmentation threshold b ; When V1+V2+…V t The first time greater than Num t When V t The corresponding CT value is recorded as the enamel segmentation threshold t .

[0030] Furthermore, the specific operation of cropping the first CT image in the aforementioned step 3 is as follows:

[0031] For the first three-dimensional CT image, 50% of the back of the brain is first cropped, leaving only the facial image. Then, for the facial image, 20% of the image on each side is cropped, 35% of the image on the top, and 10% of the image on the bottom. After the cropping is completed, the second CT image is obtained.

[0032] Furthermore, in the aforementioned step 5, based on the sum histogram of cross-sectional CT values ​​generated by each cross-sectional slice of the third CT image, the method for obtaining the lower cavity crown plane is as follows:

[0033] In the cross-sectional slices numbered [20% m] to m, the peaks are obtained according to the histogram of the sum of the cross-sectional CT values;

[0034] If there is only one peak in the histogram of the sum of cross-sectional CT values, the teeth of the upper and lower cavities are occlusal, and the number of the cross-sectional slice where the peak is located is recorded as a, and the cross-sectional slice numbered [a-5%m] is the crown plane of the lower cavity tooth;

[0035] If there are two or more peaks in the cross-sectional CT value sum histogram, the cross-sectional slice number corresponding to the highest peak is recorded as b, the cross-sectional slice number corresponding to the second highest peak is recorded as c, and the ratio of c to b is calculated as d;

[0036] If d<0.4, the teeth of the upper and lower cavities are occluded, and the cross-sectional slice numbered [b-5%m] is the crown plane of the lower cavity teeth; if d≥0.4, the teeth of the upper and lower cavities are separated, and the cross-sectional slice corresponding to the highest wave peak and the cross-sectional slice corresponding to the second highest wave peak, the cross-sectional slice with the smaller number is the crown plane of the lower cavity teeth.

[0037] In the technical solution, the operator [x] represents taking the integer part of x.

[0038] The method for adaptive dental arch region generation based on oral CBCT of the present invention adopts the above technical solution and has the following beneficial effects compared with the prior art:

[0039] 1. It can adaptively process image data of different CT value ranges scanned by different oral cone beam CT devices. It can automatically find the corresponding bone and enamel segmentation thresholds according to different CT value ranges, thereby realizing adaptive imaging of different devices, which has strong versatility.

[0040] 2. According to the different situations of whether the teeth in the upper and lower cavities are occluded, the crown plane of the lower cavity can be accurately located through adaptive methods, so as to accurately extract the dental arch area in different situations.

[0041] 3. The slices used to generate the dental arch area are interval slices extracted from the crown plane of the lower cavity toward the bottom, which includes the mandibular part and can also handle the missing teeth situation well.

[0042] 4. The present invention deletes abnormal extreme values ​​with large deviations in CT images, and has high fault tolerance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the process of the adaptive dental arch region generation method of the present invention;

[0044] Figure 2The images are of whether the teeth of the upper and lower cavities are occluded according to the embodiment of the present invention; in Figure (a), the teeth of the upper and lower cavities are not occluded, and in Figure (b), the teeth of the upper and lower cavities are occluded;

[0045] Figure 3 Schematic diagram of the position of the lower cavity crown plane identified by the present invention when the upper and lower cavity teeth are occluded or not in an embodiment of the present invention, wherein the horizontal line is the position of the lower cavity crown plane; Figure (a) is the position of the lower cavity crown plane when the upper and lower cavity teeth are not occluded, and Figure (b) is the position of the lower cavity crown plane when the upper and lower cavity teeth are occluded;

[0046] Figure 4 A maximum density projection map generated by an embodiment of the present invention;

[0047] Figure 5 A binary image generated by an embodiment of the present invention;

[0048] Figure 6 It is a binary image after the closing operation in the embodiment of the present invention;

[0049] Figure 7 Dental arch regions generated for an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to better understand the technical content of the present invention, specific embodiments are given and described as follows in conjunction with the accompanying drawings.

[0051] Various aspects of the invention are described herein with reference to the accompanying drawings, in which many illustrative embodiments are shown. The embodiments of the invention are not limited to those described in the accompanying drawings. It should be understood that the invention is implemented by any of the various concepts and embodiments described above, as well as the concepts and embodiments described in detail below, because the concepts and embodiments disclosed in the invention are not limited to any implementation. In addition, some aspects disclosed in the invention may be used alone or in any appropriate combination with other aspects disclosed in the invention.

[0052] In this embodiment, an adaptive dental arch region generation method based on oral CBCT is provided. Figure 1 As shown, the specific steps are as follows:

[0053] Step 1: Obtain CBCT images from the oral cone beam CT device;

[0054] The oral region is scanned based on an oral cone beam CT device to obtain a first CT image and acquire data of the first CT image; wherein the data of the first CT image includes a CT value of each voxel and a cross-sectional slice of the first CT image.

[0055] The image data scanned by different oral cone beam CT devices have different CT value ranges. For example, the CT value range of some oral cone beam CT devices is -1000 to 15383, and the range of some devices is 400 to 4095. The CT values ​​of soft tissues, bones, and teeth in different CT value ranges are also different, and the corresponding bone and enamel segmentation thresholds are also different. In addition, depending on the purpose of the scan, the occlusion of the upper and lower teeth during the scan is also different, such as Figure 2 As shown, if there is an occlusal bracket, the patient's teeth are bitten on the occlusal plate, and the teeth of the upper and lower cavities are separated in the image; if there is no occlusal bracket, the patient's teeth are bitten together, and the teeth of the upper and lower cavities are bitten together in the image.

[0056] Step 2: Adaptive method to obtain the segmentation threshold of bone and enamel;

[0057] Based on the CT value of each voxel in the first CT image, each CT value and the corresponding quantity are counted, and the abnormal extreme values ​​in the statistical results are first deleted, and then the bone segmentation threshold and the enamel segmentation threshold are respectively obtained by an adaptive method; wherein the bone segmentation threshold and the enamel segmentation threshold include the following steps 201 to 205:

[0058] Step 201: Count the total number of voxels Total1 of the first CT image, and use the following formula to obtain the number of extreme values ​​Num o :

[0059] Num o =[Total 1* K o ]

[0060] Among them: extreme value coefficient K o is 0.01; the operation [x] means taking the integer part of x;

[0061] Step 202: Based on the CT value of each voxel in the first CT image, all different CT values ​​after deduplication in the first CT image are sorted from small to large to obtain a first CT value sorting: C1, C2, ..., C n ; and count the number of CT values ​​corresponding to V1, V2...V n ;

[0062] Step 203: Delete abnormal extreme values ​​in the first CT image according to different situations:

[0063] If V1 is greater than Num o , then update the number of C1 to V1-Num o ;

[0064] If V1 is equal to Num o, delete C1 and its corresponding quantity, and replace C2, C3, ... n Regenerate the second order of CT values: C1, C2, C3..., the corresponding quantities are V1, V2...V n ;

[0065] If V1 is less than Num o , calculate V1+V2, V1+V2+V3, ..., until V1+V2+...V i The first time greater than Num o , delete C1, C2, ... C i-1 and the corresponding quantity, C i The quantity is updated to V1+V2+...V i -Num o ; C i , C i+1 ……C n Regenerate the second order of CT values: C1, C2, C3..., the corresponding quantities are V1, V2...V n ;

[0066] Step 204: Count the total number of voxels in the first CT image after removing the abnormal extreme value, Total2, and use the following formula to obtain the bone threshold number Num b and enamel threshold number Num t ;

[0067] Num b =[Total 2* K b ]

[0068] Num t =[Total 2* K t ]

[0069] Where: Bone threshold coefficient K b and enamel threshold coefficient K t All are empirical coefficients; the operation [x] means taking the integer part of x;

[0070] Step 205: Second sorting based on CT value: C1, C2...C n and the corresponding quantities V1, V2, ... V n , calculate V1+V2, V1+V2+V3, ... in sequence, when V1+V2+...V b The first time greater than Num b , V b The corresponding CT value is recorded as the bone segmentation threshold b ; When V1+V2+…V t The first time greater than Num tWhen V t The corresponding CT value is recorded as the enamel segmentation threshold t .

[0071] Step 3: Adaptive method locates the crown plane of the lower cavity;

[0072] First, the first CT image is cropped, leaving the facial part including the oral cavity, and the second CT image is obtained; the specific operation of cropping is as follows: for the three-dimensional first CT image, first crop off 50% of the back of the head, leaving only the facial image, and then for the facial image, crop off 20% of the image on each side, crop 35% of the image on the top, and crop 10% of the image on the bottom. After cropping, the second CT image is obtained.

[0073] Then, all voxels in the second CT image whose CT values ​​are less than the enamel segmentation threshold are screened, and their CT values ​​are all updated to 0, so as to obtain a third CT image, and at the same time, a corresponding cross-sectional slice of the third CT image is generated;

[0074] Finally, the cross-sectional slices of the third CT image are sorted from bottom to top as 1, 2, ..., m; the sum of the CT values ​​corresponding to each pixel in each cross-sectional slice is counted to generate a cross-sectional CT value sum histogram; and then the occlusion of the upper and lower teeth is determined by the peak characteristics of the cross-sectional CT value sum histogram, and the following is obtained: Figure 3 The lower cavity crown plane and the slice number n where it is located are shown; the specific judgment process is as follows:

[0075] In the cross-sectional slices numbered [20% m] to m, the peaks are obtained according to the histogram of the sum of the cross-sectional CT values;

[0076] If there is only one peak in the histogram of the sum of cross-sectional CT values, the teeth of the upper and lower cavities are occlusal, and the number of the cross-sectional slice where the peak is located is recorded as a, and the cross-sectional slice numbered [a-5%m] is the crown plane of the lower cavity tooth;

[0077] If there are two or more peaks in the cross-sectional CT value sum histogram, the cross-sectional slice number corresponding to the highest peak is recorded as b, the cross-sectional slice number corresponding to the second highest peak is recorded as c, and the ratio of c to b is calculated as d;

[0078] If d < 0.4, the teeth of the upper and lower chambers are in occlusion, and the cross-sectional slice numbered [b-5%m] is the crown plane of the lower chamber;

[0079] If d≥0.4, the teeth in the upper and lower cavities are separated. Among the cross-sectional slice corresponding to the highest wave crest and the cross-sectional slice corresponding to the second highest wave crest, the cross-sectional slice with the smaller serial number is the crown plane of the lower cavity.

[0080] Step 4: Generate the maximum density projection map;

[0081] For the first CT image, the cross-sectional slice n where the lower cavity crown plane is located obtained in step 5 is taken as the starting slice, and the cross-sectional slices numbered from n to [n-20%m] are taken as the slice interval, and the slices in the slice interval are subjected to maximum density projection to obtain a maximum density projection image; Figure 4 As shown, the maximum density projection image is a two-dimensional plane image;

[0082] Step 5: Generate a binary image;

[0083] The maximum density projection image obtained in step 6 is binarized using the bone segmentation threshold to obtain Figure 5 The binary image shown; wherein the binarization method is: for each pixel point in the maximum density projection image, if the pixel value is less than the bone segmentation threshold, then its pixel value is updated to 0; otherwise, its pixel value is updated to 1;

[0084] Step 6: Generate dental arch area;

[0085] First, the binary image obtained in step 7 is corroded to remove burrs and isolated pixels, and then the sum of pixel values ​​of each connected area in the binary image is counted, and the area where the sum of pixel values ​​is less than a preset value is removed to obtain a new binary image;

[0086] Then, the new binary image is closed to fill the small holes; Figure 6 As shown, the sum of pixel values ​​of each connected area in the new binary image is counted again, the connected area with the largest pixel value sum is retained, and other areas are deleted to obtain the largest connected area;

[0087] Finally, the holes of the largest connected area obtained in step 9 are filled, as follows Figure 7 As shown, the maximum connected area after the hole is filled, namely the dental arch area, is obtained.

[0088] Although the present invention has been described above with preferred embodiments, it is not intended to limit the present invention. A person skilled in the art of the present invention may make various modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be determined by the definition of the claims.

Claims

1. An adaptive dental arch region generation method based on oral CBCT, characterized in that: The specific steps of the method are as follows: Step 1: Scanning the oral region based on an oral cone beam CT device to obtain a first CT image and acquire data of the first CT image; wherein the data of the first CT image includes a CT value of each voxel and a cross-sectional slice of the first CT image; Step 2: Based on the CT value of each voxel in the first CT image, each CT value and its corresponding quantity are counted, and the abnormal extreme values ​​in the statistical results are first deleted, and then the bone segmentation threshold and the enamel segmentation threshold are respectively obtained by an adaptive method; In step 2, the bone segmentation threshold and the enamel segmentation threshold are obtained through steps 201 to 205: Step 201: Count the total number of voxels Total1 of the first CT image, and use the following formula to obtain the number of extreme values ​​Num o : In a o =[Total 1* K o ] Among them: extreme value coefficient K o is 0.01; the operation [x] means taking the integer part of x; Step 202: Based on the CT value of each voxel in the first CT image, all different CT values ​​after deduplication in the first CT image are sorted from small to large to obtain a first CT value sorting: C1, C2, ..., C n ; and count the number of CT values ​​corresponding to V1, V2...V n ; Step 203: Delete abnormal extreme values ​​in the first CT image according to different situations: If V1 is greater than Num o , then update the number of C1 to V1-Num o ; If V1 is equal to Num o , delete C1 and its corresponding quantity, and replace C2, C3, ... n Regenerate the second order of CT values: C1, C2, C3..., the corresponding quantities are V1, V2...V n ; If V1 is less than Num o , calculate V1+V2, V1+V2+V3, ..., until V1+V2+...V i The first time greater than Num o , delete C1, C2, ... C i-1 and the corresponding quantity, C i The quantity is updated to V1+V2+...V i -Num o ; C i , C i+1 ……C n Regenerate the second order of CT values: C1, C2, C3..., the corresponding quantities are V1, V2...V n ; Step 204: Count the total number of voxels in the first CT image after removing the abnormal extreme value, Total2, and use the following formula to obtain the bone threshold number Num b and enamel threshold number Num t ; In a b =[Total 2* K b ] In a t =[Total 2* K t ] Where: Bone threshold coefficient K b and enamel threshold coefficient K t All are empirical coefficients; the operation [x] means taking the integer part of x; Step 205: Second sorting based on CT value: C1, C2...C n and the corresponding quantities V1, V2, ... V n , calculate V1+V2, V1+V2+V3, ... in sequence, when V1+V2+...V b The first time greater than Num b , V b The corresponding CT value is recorded as the bone segmentation threshold b ; When V1+V2+…V t The first time greater than Num t When V t The corresponding CT value is recorded as the enamel segmentation threshold t ; Step 3: crop the first CT image, leaving the facial part including the oral cavity, to obtain a second CT image; Step 4: Filter all voxels in the second CT image whose CT values ​​are less than the enamel segmentation threshold, and update their CT values ​​to 0, to obtain a third CT image, and generate corresponding cross-sectional slices of the third CT image; Step 5: Sort the cross-sectional slices of the third CT image from bottom to top as 1, 2, ..., m; perform statistics on the sum of the CT values ​​corresponding to each pixel in each cross-sectional slice to generate a cross-sectional CT value sum histogram; then determine the tooth occlusion of the upper and lower cavities by the peak characteristics of the cross-sectional CT value sum histogram, and obtain the lower cavity crown plane and the cross-sectional slice number n where it is located; wherein the abscissa of the cross-sectional CT value sum histogram is the cross-sectional slice number, and the ordinate is the sum of the CT values ​​corresponding to each pixel in the corresponding cross-sectional slice; Step 6: For the first CT image, take the cross-sectional slice n where the lower cavity crown plane is located obtained in step 5 as the starting slice, take the cross-sectional slices numbered from n to [n-20%m] as the slice interval, perform maximum density projection on the slices in the slice interval, and obtain a maximum density projection map; wherein the maximum density projection map is a two-dimensional plane map; Step 7: binarize the maximum density projection image obtained in step 6 using the bone segmentation threshold to obtain a binary image; wherein the binarization method is: for each pixel point in the maximum density projection image, if the pixel value is less than the bone segmentation threshold, update its pixel value to 0; otherwise, update its pixel value to 1; Step 8: Corrode the binary image obtained in step 7 to remove burrs and isolated pixels, and then count the sum of pixel values ​​of each connected area in the binary image, remove the area where the sum of pixel values ​​is less than a preset value, and obtain a new binary image; Step 9: Perform a closing operation on the new binary image to fill the small holes; again count the sum of the pixel values ​​of each connected area in the new binary image, retain the connected area with the largest pixel value sum, delete other areas, and obtain the largest connected area; Step 10: Fill the holes in the maximum connected area obtained in step 9 to obtain the maximum connected area after hole filling, that is, the dental arch area.

2. The method for adaptive dental arch region generation based on oral CBCT according to claim 1, characterized in that: The specific operation of cropping the first CT image in step 3 is as follows: For the first three-dimensional CT image, 50% of the back of the brain is first cropped, leaving only the facial image. Then, for the facial image, 20% of the image on each side is cropped, 35% of the image on the top, and 10% of the image on the bottom. After the cropping is completed, the second CT image is obtained.

3. The method for adaptive dental arch region generation based on oral CBCT according to claim 1, characterized in that: In step 5, the method for obtaining the lower cavity crown plane based on the sum histogram of cross-sectional CT values ​​generated by each cross-sectional slice of the third CT image is as follows: In the cross-sectional slices numbered [20% m] to m, the peaks are obtained according to the histogram of the sum of the cross-sectional CT values; If there is only one peak in the histogram of the sum of cross-sectional CT values, the teeth of the upper and lower cavities are occlusal, and the number of the cross-sectional slice where the peak is located is recorded as a, and the cross-sectional slice numbered [a-5%m] is the crown plane of the lower cavity tooth; If there are two or more peaks in the cross-sectional CT value sum histogram, the cross-sectional slice number corresponding to the highest peak is recorded as b, the cross-sectional slice number corresponding to the second highest peak is recorded as c, and the ratio of c to b is calculated as d; If d < 0.4, the teeth of the upper and lower chambers are in occlusion, and the cross-sectional slice numbered [b-5%m] is the crown plane of the lower chamber; If d≥0.4, the teeth in the upper and lower cavities are separated. Among the cross-sectional slice corresponding to the highest wave crest and the cross-sectional slice corresponding to the second highest wave crest, the cross-sectional slice with the smaller serial number is the crown plane of the lower cavity.

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