CN110889850B - CBCT tooth image segmentation method based on central point detection - Google Patents

CBCT tooth image segmentation method based on central point detection Download PDF

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CN110889850B
CN110889850B CN201911279019.3A CN201911279019A CN110889850B CN 110889850 B CN110889850 B CN 110889850B CN 201911279019 A CN201911279019 A CN 201911279019A CN 110889850 B CN110889850 B CN 110889850B
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李纯明
刘祎
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University of Electronic Science and Technology of China
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Abstract

The invention provides a CBCT tooth image segmentation method based on central point detection, which comprises the steps of preprocessing; coarse segmentation; performing double-level set fine segmentation; performing interlayer iterative segmentation; the three-dimensional structure of the tooth. The tooth image segmentation algorithm based on the center point detection fully utilizes the detected center point information to carry out rough segmentation of an initial layer, and replaces manual initialization with a rough segmentation result, so that the image segmentation under the guidance of a full-automatic expert is realized. The tooth image segmentation algorithm based on the central point detection is provided by taking the detected central point information as the prior information of the segmentation algorithm; and a double-level set segmentation algorithm and threshold optimization constraint processing are introduced in the segmentation process, so that the tooth three-dimensional structure can be obtained more quickly, conveniently and accurately, the segmentation is more accurate, and the robustness is better.

Description

CBCT dental image segmentation method based on central point detection
Technical Field
The invention belongs to the technical field of image segmentation, and particularly relates to a CBCT tooth segmentation method based on central point detection.
Background
As society develops, more and more adults seek orthodontic treatment to improve their smile, correct dental bite conditions or correct other problems caused by injury, disease or prolonged neglect of oral care. The medical imaging technology becomes an indispensable technical means for comprehensively, accurately and accurately acquiring the data of the patient and further providing powerful guarantee for the diagnosis and treatment of oral diseases. The oral cavity CT equipment is a revolutionary tool of dentistry, and the CBCT is applied to the field of oral medicine from the late stage of the nineties of the last century, so that the three-dimensional structure of a jaw face can be truly reflected, the defect of two-dimensional imaging is overcome, the relationship between teeth and bones can be evaluated more three-dimensionally, and a more reasonable scheme can be formulated by an orthodontist. However, because the CBCT tooth slice image has low brightness, poor contrast, more noise points, unobvious boundaries and abnormal and complex structure, the individual is diversified; in addition, the CT is affected by noise, artifacts caused by non-uniformity of intensity, and the same intensity of different tissues, so that automatic segmentation of teeth is very difficult, and most images have non-uniform gray levels. How to process an effective segmented dental image becomes a key step in order to obtain dental information.
Currently, the main tooth segmentation algorithms are: manual segmentation has the disadvantages that multiple persons are required to participate, the segmentation time is long, and the measurement precision is influenced by the operation of an operator; self-adaptive threshold segmentation is adopted, but the problems of more noise points, fuzzy adjacent tooth boundaries and uneven tooth gray values exist, so that the interference of ineffective information is very easy to occur; morphological segmentation, which often occurs over-segmentation due to over-sensitivity to edges; level set segmentation based on active contour models, such as DRLSE and MICO, has the problems that the requirement for initialization is too high, the requirement depends on accurate priori knowledge, and local control on an evolution surface is lacked. Due to imaging technology defects of medical images and particularity of tooth structures, the segmentation processing of teeth can be realized only by means of a certain amount of interactive operation at present. Therefore, introducing shape priors to simplify the segmentation problem is essential. In order to realize accurate and automatic segmentation of the ROI, the detected central point information is taken as the prior information of a segmentation algorithm, and a tooth image segmentation algorithm based on central point detection is provided, so that the segmentation is more accurate and the robustness is better. The tooth image segmentation algorithm based on the central point detection fully utilizes the detected central point information to carry out rough segmentation of an initial layer, replaces manual initialization with a rough segmentation result, and is directly blended into an image to be segmented through prior information of a key area, so that full-automatic image segmentation under the guidance of an expert is realized.
Disclosure of Invention
Aiming at the defects in the prior art, the CBCT tooth segmentation method based on the central point detection provided by the invention solves the problems that most of the existing tooth segmentation algorithms need manual initialization and the tooth segmentation precision is improved in combination.
In order to achieve the above purpose, the invention adopts the technical scheme that:
the scheme provides a CBCT tooth image segmentation method based on central point detection, which comprises the following steps:
s1, pretreatment: acquiring an original image of the tooth, and calculating the size of a region wrapping the tooth by using a MIP algorithm according to the original image of the tooth;
s2, rough segmentation: selecting a proper initial layer according to the size of the region wrapping the teeth, and calculating by using a watershed algorithm to obtain a rough segmentation result of the initial layer of the teeth;
s3, bi-level set fine segmentation: taking the rough segmentation result as initialization, and carrying out fine segmentation processing on the initial tooth layer by using a double-level set DRLSE model to obtain two-dimensional segmentation results of the upper and lower rows of initial tooth layers;
s4, interlayer iteration segmentation: performing optimal threshold processing on the two-dimensional segmentation results of the upper and lower rows of tooth initial layers by utilizing interlayer information, and performing upward or downward layer-by-layer iteration by utilizing a double-level set DRLSE model according to the processing result to obtain a two-dimensional segmentation result of each layer of the teeth of the CBCT image;
s5, outputting a three-dimensional tooth structure: and (3) carrying out segmentation processing on the two-dimensional segmentation result of the CBCT image tooth by utilizing the DRLSE model to obtain a three-dimensional segmentation result of the CBCT tooth image, thereby completing the segmentation of the CBCT tooth image.
Further, the step S1 includes the steps of:
s101, obtaining a tooth original image, storing the tooth original image in a DICOM format, and reading tooth slice images layer by layer;
s102, carrying out piecewise linear transformation processing on the tooth original image, and normalizing the gray level of the tooth original image to [0,255 ];
s103, respectively projecting the image subjected to gray level normalization processing in the x direction, the y direction and the z direction by using an MIP (maximum intensity projection) algorithm to obtain the size of a region wrapping the teeth;
the expression for projecting the three directions x, y and z of the image respectively is as follows:
xmip(j,k)＝max(xmip(j,k),a(i,j,k))；
ymip(i,k)＝max(ymip(i,k),a(i,j,k))；
zmip(i,j)＝max(zmip(i,j),a(i,j,k))；
where xmip (j, k) denotes the projection of the image in the x direction, ymip (i, k) denotes the projection of the image in the y direction, zmip (i, j) denotes the projection of the image in the z direction, i denotes the image size ranging from 1 to the x direction, j denotes the image size ranging from 1 to the y direction, k denotes the image size ranging from 1 to the z direction, and a (i, j, k) denotes the grayscale size of the image at i, j, and k.
Still further, the step S2 includes the following steps:
s201, respectively selecting initial layers of upper and lower rows of teeth according to the size of the area wrapping the teeth;
s202, respectively detecting central points of the upper row of teeth and the lower row of teeth according to the initial layers of the upper row of teeth and the lower row of teeth, taking the central points as a foreground, and taking a preset threshold value as a background;
s203, marking the foreground and the background inside and outside by using a watershed algorithm, and obtaining rough segmentation results of the upper row of teeth and the lower row of teeth respectively.
Still further, the step S3 includes the following steps:
s301, according to the rough segmentation result obtained in the step S2, performing two initialization treatments of double-level sets on the upper row of teeth and the lower row of teeth according to the alternating sequence of the teeth to obtain the initialization of the initial layers of the upper row of teeth and the lower row of teeth;
s302, the two-level set DRLSE model is used for respectively carrying out fine segmentation on the upper row of tooth initial layers and the lower row of tooth initial layers, and therefore two-dimensional segmentation results of the upper row of tooth initial layers and the lower row of tooth initial layers are obtained.
Still further, the step S4 includes the following steps:
s401, performing optimal threshold processing on the two-dimensional segmentation results of the upper and lower tooth initial layers by utilizing interlayer information, and taking the processed results as initialization of the upper layer or the lower layer;
s402, sequentially performing iterative segmentation processing on the upper row of teeth and the lower row of teeth by using a double-level set DRLSE model, so as to obtain a two-dimensional segmentation result of each layer of the teeth of the CBCT image.
Still further, the expression for performing the optimal threshold processing in step S401 is as follows:
Figure GDA0003698253900000041
wherein x isiRepresents [0,255]]Pixel gray scale of (2), yiThe number of pixel points of the gray level in the image curve is represented, A represents the amplitude, S represents the curve width, e represents a natural constant, and mu represents a parameter for controlling evolution.
Still further, the step S402 specifically includes:
and respectively segmenting the upper row of teeth and the lower row of teeth layer by layer from the initial layer to the tooth crown direction and segmenting the upper row of teeth and the lower row of teeth layer by layer from the initial layer to the tooth root direction by utilizing a double-level set DRLSE model so as to obtain a two-dimensional segmentation result of the CBCT image teeth.
Still further, the two bilevel set evolution curves in each iteration segmentation process in step S302 and step S402 need to satisfy the following conditions:
φ1＝max(φ1,-φ2)
φ2＝max(-φ1,φ2)
wherein phi1Represents a first horizontal curve, phi2A second horizontal curve is shown, max (-) indicates the maximum value.
Still further, the expressions of the step S302 and the step S402 segmented by using the dual level set DRLSE model are as follows:
Figure GDA0003698253900000051
Figure GDA0003698253900000052
Figure GDA0003698253900000053
Figure GDA0003698253900000054
Figure GDA0003698253900000055
wherein, the first and the second end of the pipe are connected with each other,
Figure GDA0003698253900000056
the expressions all represent a time evolution equation, mu, lambda and alpha all represent parameters for controlling evolution, delta represents a unit impulse function, div (·) represents divergence, phi1Represents a first horizontal curve, phi2A second curve of the level is shown,
Figure GDA0003698253900000057
representing the corresponding gradients of the two level sets, dp(s) denotes a function defined by a parameter s, p'(s) denotes a derivation of the function p, s denotes [0,1 ]]P denotes a function defined by a distance regularization term, p2(. cndot.) represents a function of the level set gradient norm,
Figure GDA0003698253900000058
the gradient mode representing the level set curve,
Figure GDA0003698253900000059
representing gradient operators, GσDenotes a gaussian function with standard deviation σ, I denotes the entire image, x denotes a convolution operator, and g denotes an edge detection factor.
Still further, step S5 is specifically:
s501, stacking two-dimensional segmentation results of single-layer CBCT tooth images of upper and lower rows of teeth, and taking the stacking results as initialization of a three-dimensional DRLSE model;
and S502, carrying out iterative processing on the stacking result by using a three-dimensional DRLSE model to obtain a three-dimensional segmentation result of the CBCT dental image, thereby completing the segmentation of the CBCT dental image.
The invention has the beneficial effects that:
(1) the method takes the detected tooth center point as prior information of a coarse segmentation algorithm, obtains the rough boundary of the teeth in the image by utilizing the prior information of a foreground region, and takes the rough boundary as the initialization of a level set model. Therefore, the time waste caused by manual intervention of doctors and the unreasonable problem of manual initialization can be reduced;
(2) the invention can effectively reduce the noise sensitivity of the function by the operation of the constrained bi-level set function by means of the information between layers, prevent the inaccurate division caused by the attraction of adjacent teeth, and can fully utilize the tooth region information to effectively divide the full oral cavity teeth by the bi-level set to obtain better division results.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a CBCT image of the tooth in this embodiment.
Fig. 3 is a schematic diagram of the rough segmentation result in this embodiment.
Fig. 4 is a graph showing the result of the dual level set single layer segmentation in this embodiment.
Fig. 5 is a schematic diagram of the tooth division process in this embodiment.
Fig. 6 is a schematic diagram of the curve internal gray level gaussian fitting in this embodiment.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
Examples
The method aims at the situation that most of the existing tooth segmentation algorithms need manual initialization and improve tooth segmentation precision. The invention provides an idea for effectively improving the automation degree, and introduces a double-level set and constraint processing in the whole process of outputting the three-dimensional tooth structure segmentation, so that the tooth three-dimensional result can be obtained more quickly, conveniently and accurately. The tooth image segmentation algorithm based on the central point detection is provided by taking the detected central point information as the prior information of the segmentation algorithm, so that the segmentation is more accurate and the robustness is better. The tooth image segmentation algorithm based on the central point detection fully utilizes the detected central point information to carry out rough segmentation of an initial layer, replaces manual initialization with a rough segmentation result, and directly blends the prior information of a key area into an image to be segmented to realize full-automatic image segmentation under the guidance of an expert. The invention aims at the situation that most of the existing tooth segmentation algorithms need manual initialization and are combined with improvement of tooth segmentation precision.
As shown in FIG. 1, the invention discloses a CBCT dental image segmentation method based on central point detection, which is realized by the following steps:
s1, pretreatment: acquiring a tooth original image, and calculating the size of a region wrapping the tooth by using a MIP algorithm according to the tooth original image, wherein the implementation method comprises the following steps:
s101, obtaining a tooth original image, storing the tooth original image in a DICOM format, and reading tooth slice images layer by layer;
s102, carrying out piecewise linear transformation processing on the tooth original image, and normalizing the gray level of the tooth original image to [0,255 ];
and S103, respectively projecting the image subjected to gray level normalization processing in the x direction, the y direction and the z direction by using an MIP (maximum intensity projection) algorithm to obtain the size of the region wrapping the teeth.
In this embodiment, an original image is first acquired, stored in a standard DICOM format, and the dental slice images are read layer by layer. And the gray level histogram of the original image is obtained, the image gray level is normalized to [0,255] by carrying out piecewise linear transformation, and then the size of the wrapped tooth area is obtained by processing the original image by using the MIP algorithm, so that the size of the processed image is reduced, and the segmentation speed is convenient to improve.
In this embodiment, as shown in fig. 2, in the step S1, the gray histogram of the obtained original image has a larger gray range of approximately 3000, and for convenience of image processing, the gray is converted to [0,255], and then, since the single-layer picture is too large, the processing speed is slowed, so that the MIP projection algorithm is used to respectively project the x, y, and z directions, and the original image size eg:512 × 512 × 512 × 512 is processed to obtain the size eg:250 × 300 × 280) of the wrapped tooth region. Maximum Intensity Projection (MIP): as the name implies, the maximum value is projected. That is, the gray level is the highest in the pixel block through which each ray passes is calculated as the output result. For the case of three directions x, y and z:
xmip(j,k)＝max(xmip(j,k),a(i,j,k))；
ymip(i,k)＝max(ymip(i,k),a(i,j,k))；
zmip(i,j)＝max(zmip(i,j),a(i,j,k))；
where xmip (j, k) denotes the projection of the image in the x direction, ymip (i, k) denotes the projection of the image in the y direction, zmip (i, j) denotes the projection of the image in the z direction, i denotes the image size ranging from 1 to the x direction, j denotes the image size ranging from 1 to the y direction, k denotes the image size ranging from 1 to the z direction, and a (i, j, k) denotes the grayscale size of the image at i, j, and k.
S2, rough segmentation: selecting a proper initial layer according to the size of the region wrapping the teeth, and calculating by using a watershed algorithm to obtain a rough segmentation result of the initial layer of the teeth, wherein the realization method comprises the following steps:
s201, respectively selecting initial layers of upper and lower rows of teeth according to the size of the area wrapping the teeth;
s202, respectively detecting central points of the upper row of teeth and the lower row of teeth according to the initial layers of the upper row of teeth and the lower row of teeth, taking the central points as a foreground, and taking a preset threshold value as a background;
s203, marking the foreground and the background inside and outside by using a watershed algorithm, and obtaining rough segmentation results of the upper row of teeth and the lower row of teeth respectively.
In this embodiment, a suitable initial layer is selected first; the central point obtained by the detection of the initial layer is used as the foreground, the threshold value is used as the background, the coarse segmentation result of the initial layer is obtained by using the control mark watershed algorithm, and a better initialization that the teeth are not adhered is ensured to be obtained.
In this embodiment, there are more adhesions between teeth in step S2, and in order to distinguish the possibility of tooth adhesion, we select as initial layers as many as possible from the crown layer to the intermediate layer having the periodontal tissue appearance layer. But since the condition of a single layer of 16 teeth of a human body is considered, the initial layer ensures that each tooth is not adhered to each other as much as possible; therefore, before fine segmentation, a central point obtained by detection of an initial layer is used as a foreground, a threshold value is used as a background, and a coarse segmentation result of the initial layer is obtained by using a control mark watershed algorithm, so that a good initialization that teeth are not adhered can be obtained. The watershed transform obtains a catchbasin image of the input image, and boundary points between catchbasins are watershed. It is clear that the watershed represents the input image maximum point. Therefore, to obtain edge information of an image, a gradient image is usually taken as an input image, namely:
g(x,y)＝grad(f(x,y))
where f (x, y) represents the original image and grad (·) represents the gradient operation. But due to excessive segmentation caused by noise and other irregularities in the gradient, labels are introduced, with foreground and background labeled inside and outside, respectively. As shown in fig. 3, a better result of rough tooth segmentation can be obtained.
S3, bi-level set fine segmentation: as shown in fig. 4, the rough segmentation result is used as initialization, and the dual level set DRLSE model is used to perform fine segmentation on the initial tooth layer, so as to obtain two-dimensional segmentation results of the initial tooth layers in the upper and lower rows, which is implemented as follows:
s301, according to the rough segmentation result obtained in the step S2, performing two initialization treatments of double-level sets on the upper row of teeth and the lower row of teeth according to the alternating sequence of the teeth to obtain the initialization of the initial layers of the upper row of teeth and the lower row of teeth;
s302, the two-level set DRLSE model is used for finely dividing the initial layers of the upper row of teeth and the lower row of teeth respectively, and therefore two-dimensional division results of the initial layers of the upper row of teeth and the lower row of teeth are obtained.
In this embodiment, the DRLSE model is used to obtain the tooth segmentation result of the initial layer, and the result of the previous layer is used as the initialization of the next layer by using the interlayer information, so as to perform iterative segmentation in sequence. Because the root division problem of the same tooth cannot be well solved by a single level set method for the positions of the molars, the optimal threshold value method is carried out on the segmentation result of the previous layer in the iterative segmentation process between layers for optimization processing. In addition, the situation that the segmentation result of two similar teeth is attracted by the adjacent teeth is probably caused by segmenting each tooth by using a single horizontal set, in order to ensure the segmentation precision of the single tooth, a double-horizontal-set method is adopted for segmentation processing, and the double-horizontal-set is subjected to intersection processing in each iteration process, so that the situation that two curves are staggered or fused into a boundary in the evolution process is avoided, and the segmentation result is favorably improved. And solving the level set function in an iterative mode to obtain the segmentation result of each layer of the upper and lower rows of teeth.
In this embodiment, the main idea of step S3 is to select appropriate initial layers for the upper and lower rows of teeth, divide the lower row of teeth from the initial layers to the tooth roots, and divide from the initial layers to the tooth crowns; the upper row of teeth are segmented from the initial layer to the crown and then segmented from the initial layer to the root, a schematic diagram of the segmentation process is shown in figure 5, a rough segmentation result is obtained, and finally, the segmentation result of each layer is subjected to three-dimensional reconstruction.
The fine segmentation model utilizes a dual level set DRLSE model, which is as follows:
Figure GDA0003698253900000101
Figure GDA0003698253900000102
Figure GDA0003698253900000103
Figure GDA0003698253900000104
Figure GDA0003698253900000105
wherein the content of the first and second substances,
Figure GDA0003698253900000106
the expressions all represent a time evolution equation, mu, lambda and alpha all represent parameters for controlling evolution, delta represents a unit impulse function, div (-) represents divergence, phi1Represents a first horizontal curve, phi2A second curve of the level is shown,
Figure GDA0003698253900000107
representing the corresponding gradients of the two level sets, dp(s) denotes a function defined by a parameter s, p'(s) denotes a derivation of the function p, s denotes [0,1 ]]P denotes a function defined by a distance regularization term, p2(. cndot.) represents a function of the level set gradient norm,
Figure GDA0003698253900000108
the gradient mode of the level set curve is represented,
Figure GDA0003698253900000111
representing gradient operators, GσDenotes a gaussian function with standard deviation σ, I denotes the entire image, x denotes a convolution operator, and g denotes an edge detection factor.
In this embodiment, two bilevel set evolution curves need to be guaranteed:
φ1＝max(φ1,-φ2)
φ2＝max(-φ1,φ2)
wherein phi is1Represents a first horizontal curve, phi2And (3) representing a second horizontal curve, wherein max (·) represents taking a maximum value, so that mutual exclusion of the two level set functions in the iterative evolution process of the level set functions can be ensured, and the aim of separating two adjacent teeth is fulfilled, and 16 teeth on each layer of the whole oral cavity can be segmented according to a single-double alternating mode to obtain a two-dimensional segmentation result of the single-layer CBCT tooth image.
Layer initialization threshold processing: as shown in fig. 6, the curve interior gray levels are gaussian fitted and the data points are fitted to a gaussian function type, even though the following formula:
Figure GDA0003698253900000112
to the pixel point data (x) in the curvei,yi) (i ═ 1,2, 3.), wherein xiRepresents [0,255]]Pixel gray scale of (2), yiThe number of pixel points of the gray level in an image curve is represented, A represents an amplitude value, S represents a curve width, e represents a natural constant, and mu represents a parameter for controlling evolution.
In this embodiment, gaussian fitting is performed on the internal gray level distribution of the previous layer fine segmentation result curve, and T ═ μ -3 σ is selected according to a 3 σ criterion to be used as the internal threshold of the current layer result for processing, so that segmentation accuracy is improved, interference of peripheral regions is reduced, or the situation that the previous layer result is attracted by adjacent teeth due to level set iteration occurs.
S4, interlayer iteration segmentation: performing optimal threshold processing on the two-dimensional segmentation results of the initial layers of the upper row and the lower row of teeth by utilizing interlayer information, and performing upward or downward layer-by-layer iteration by utilizing a double-level set DRLSE model according to the processing result to obtain the two-dimensional segmentation result of each layer of the teeth of the CBCT image, wherein the implementation method comprises the following steps:
s401, performing optimal threshold processing on the two-dimensional segmentation results of the upper and lower tooth initial layers by utilizing interlayer information, and taking the processed results as initialization of the upper layer or the lower layer;
s402, sequentially performing iterative segmentation processing on the upper row of teeth and the lower row of teeth by using a double-level set DRLSE model so as to obtain a two-dimensional segmentation result of each layer of the teeth of the CBCT image;
the expression for performing the optimal threshold processing in step S401 is as follows:
Figure GDA0003698253900000121
wherein x isiRepresents [0,255]]Pixel gray scale of yiExpressing the number of pixel points of the gray level in an image curve, A expressing an amplitude value, S expressing a curve width, e expressing a natural constant, mu expressing a parameter for controlling evolution, and then selecting a threshold value according to a 3 sigma criterion, wherein sigma is a standard deviation;
the step S402 specifically includes:
and respectively segmenting the upper row of teeth and the lower row of teeth layer by layer from the initial layer to the tooth crown direction and segmenting the upper row of teeth and the lower row of teeth layer by layer from the initial layer to the tooth root direction by using a double-level set DRLSE model, thereby obtaining a two-dimensional segmentation result of the teeth of the CBCT image.
S5, outputting a three-dimensional tooth structure: and (3) carrying out segmentation processing on the two-dimensional segmentation result of the CBCT image tooth by utilizing a DRLSE model to obtain a three-dimensional segmentation result of the CBCT tooth image, thereby completing the segmentation of the CBCT tooth image, wherein the implementation method comprises the following steps:
s501, stacking two-dimensional segmentation results of single-layer CBCT tooth images of upper and lower rows of teeth, and taking the stacking results as initialization of a three-dimensional DRLSE model;
s502, carrying out iterative processing on the stacking result by using a three-dimensional DRLSE model to obtain a three-dimensional segmentation result of the CBCT dental image, thereby completing the segmentation of the CBCT dental image.
In this embodiment, the obtained single-layer segmentation result is used as initialization of the three-dimensional DRLSE model, and the three-dimensional level set function is solved in an iterative manner to obtain an accurate three-dimensional segmentation result.
In this embodiment, the step S5 stacks the two-dimensional results obtained in the step S4 as the initialization of the three-dimensional DRLSE, and the three-dimensional DRLSE model is used to perform the final three-dimensional accurate segmentation processing, so that the segmentation result can be optimized, and small holes and small gaps caused by the two-dimensional stacking result can be filled up, so that the final three-dimensional tooth segmentation result is more accurate.

Claims (10)

1. A CBCT tooth image segmentation method based on central point detection is characterized by comprising the following steps:
s1, preprocessing: acquiring an original image of a tooth, and calculating the size of a region wrapping the tooth by utilizing a maximum density projection (MIP) algorithm according to the original image of the tooth;
s2, rough segmentation: selecting an initial layer according to the size of the region wrapping the teeth, and calculating by using a watershed algorithm to obtain a rough segmentation result of the initial layer of the teeth;
s3, bi-level set fine segmentation: taking the rough segmentation result as initialization, and carrying out fine segmentation processing on the initial tooth layer by using a double-level set DRLSE model to obtain two-dimensional segmentation results of the upper and lower rows of initial tooth layers;
s4, interlayer iteration segmentation: performing optimal threshold processing on the two-dimensional segmentation results of the upper and lower rows of tooth initial layers by utilizing interlayer information, and performing upward or downward layer-by-layer iteration by utilizing a double-level set DRLSE model according to the processing result to obtain a two-dimensional segmentation result of each layer of the teeth of the CBCT image;
s5, outputting a three-dimensional tooth structure: and carrying out segmentation processing on the two-dimensional segmentation result of the CBCT image tooth by using the DRLSE model to obtain a three-dimensional segmentation result of the CBCT image tooth, thereby completing the segmentation of the CBCT image tooth.
2. The CBCT dental image segmentation method based on central point detection according to claim 1, wherein the step S1 includes the steps of:
s101, obtaining original tooth images, storing the original tooth images in a DICOM (digital imaging and communications in medicine) format, and reading tooth slice images layer by layer;
s102, carrying out piecewise linear transformation processing on the tooth original image, and normalizing the gray level of the tooth original image to [0,255 ];
s103, projecting the image subjected to gray level normalization processing in the x direction, the y direction and the z direction respectively by using a Maximum Intensity Projection (MIP) projection algorithm to obtain the size of a region wrapping the teeth;
the expression for projecting the three directions x, y and z of the image respectively is as follows:
xmip(j,k)＝max(xmip(j,k),a(i,j,k))；
ymip(i,k)＝max(ymip(i,k),a(i,j,k))；
zmip(i,j)＝max(zmip(i,j),a(i,j,k))；
where xmip (j, k) denotes the projection of the image in the x direction, ymip (i, k) denotes the projection of the image in the y direction, zmip (i, j) denotes the projection of the image in the z direction, i denotes the image size ranging from 1 to the x direction, j denotes the image size ranging from 1 to the y direction, k denotes the image size ranging from 1 to the z direction, and a (i, j, k) denotes the grayscale size of the image at i, j, and k.
3. The CBCT dental image segmentation method based on center point detection as claimed in claim 1, wherein the step S2 includes the steps of:
s201, respectively selecting initial layers of upper and lower rows of teeth according to the size of the area wrapping the teeth;
s202, respectively detecting central points of the upper row of teeth and the lower row of teeth according to the initial layers of the upper row of teeth and the lower row of teeth, taking the central points as a foreground, and taking a preset threshold value as a background;
s203, marking the foreground and the background inside and outside by using a watershed algorithm, and obtaining rough segmentation results of the upper row of teeth and the lower row of teeth respectively.
4. The CBCT dental image segmentation method based on central point detection according to claim 1, wherein the step S3 includes the steps of:
s301, according to the rough segmentation result obtained in the step S2, performing two initialization treatments of a double-level set on the upper row of teeth and the lower row of teeth according to the alternating sequence of the teeth to obtain the initialization of the initial layers of the upper row of teeth and the lower row of teeth;
s302, the two-level set DRLSE model is used for finely dividing the initial layers of the upper row of teeth and the lower row of teeth respectively, and therefore two-dimensional division results of the initial layers of the upper row of teeth and the lower row of teeth are obtained.
5. The CBCT dental image segmentation method based on center point detection as claimed in claim 4, wherein the step S4 includes the steps of:
s401, performing optimal threshold processing on the two-dimensional segmentation results of the upper and lower tooth initial layers by utilizing interlayer information, and taking the processed results as initialization of the upper layer or the lower layer;
s402, sequentially performing iterative segmentation processing on the upper row of teeth and the lower row of teeth by using a double-level set DRLSE model, so as to obtain a two-dimensional segmentation result of each layer of the teeth of the CBCT image.
6. The CBCT dental image segmentation method based on central point detection according to claim 5, wherein the expression of the optimal threshold processing in step S401 is as follows:
Figure FDA0003698253890000031
wherein x isiRepresents [0,255]Pixel gray scale of (2), yiThe number of pixel points of the gray level in an image curve is represented, A represents an amplitude value, S represents a curve width, e represents a natural constant, and mu represents a parameter for controlling evolution.
7. The CBCT dental image segmentation method based on central point detection according to claim 5, wherein the step S402 specifically comprises:
and respectively segmenting the upper row of teeth and the lower row of teeth layer by layer from the initial layer to the tooth crown direction and segmenting the upper row of teeth and the lower row of teeth layer by layer from the initial layer to the tooth root direction by utilizing a double-level set DRLSE model so as to obtain a two-dimensional segmentation result of the CBCT image teeth.
8. The CBCT dental image segmentation method based on center point detection as claimed in claim 6, wherein the two bilevel set evolution curves in each iterative segmentation process in the steps S302 and S402 satisfy the following conditions:
φ1＝max(φ1,-φ2)
φ2＝max(-φ1,φ2)
wherein phi1Represents a first horizontal curve, phi2A second horizontal curve is shown, max (-) indicates maximum value.
9. The CBCT dental image segmentation method based on center point detection according to claim 8, wherein the expressions of the segmentation of step S302 and step S402 using the dual level set DRLSE model are as follows:
Figure FDA0003698253890000041
Figure FDA0003698253890000042
Figure FDA0003698253890000043
Figure FDA0003698253890000044
Figure FDA0003698253890000045
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003698253890000046
the expressions all represent a time evolution equation, mu, lambda and alpha all represent parameters for controlling evolution, delta represents a unit impulse function, div (-) represents divergence, phi1Represents a first horizontal curve, phi2A second curve of the level is shown,
Figure FDA0003698253890000047
representing the corresponding gradients of the two level sets, dp(s) denotes a function defined by a parameter s, p'(s) denotes a derivation of the function p, s denotes [0,1 ]]P denotes a function defined by a distance regularization term, p2(. cndot.) represents a function of the level set gradient norm,
Figure FDA0003698253890000048
the gradient mode of the level set curve is represented,
Figure FDA0003698253890000049
representing gradient operators, GσDenotes a gaussian function with standard deviation σ, I denotes the entire image, x denotes a convolution operator, and g denotes an edge detection factor.
10. The CBCT dental image segmentation method based on central point detection as claimed in claim 1, wherein the step S5 is specifically as follows:
s501, stacking two-dimensional segmentation results of single-layer CBCT tooth images of upper and lower rows of teeth, and taking the stacking results as initialization of a three-dimensional DRLSE model;
s502, carrying out iterative processing on the stacking result by using a three-dimensional DRLSE model to obtain a three-dimensional segmentation result of the CBCT dental image, thereby completing the segmentation of the CBCT dental image.
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