3D image processing apparatus, 3D image processing method, and program

Abstract

The present invention provides a three-dimensional image processing device that generates image data of a three-dimensional image to be used to analyze a diagnosis target. The three-dimensional image processing device is provided with an image processing unit that performs alignment between a three-dimensional image in which an analysis target photographed at a first timing appears and a three-dimensional image in which the analysis target photographed at a second timing different from the first timing appears. In each of the three-dimensional images, an image of an object in a predefined space including the analysis target appears. There is a diagnosis target in the space, and the analysis target is a support part. The alignment includes: first registration processing for performing rigid transformation with respect to one of the two three-dimensional images so that the difference between the one three-dimensional image and the other three-dimensional image becomes small; and second registration processing for performing rigid transformation with respect to the one three-dimensional image so that the difference between an image of the analysis target appearing in the other three-dimensional image and an image of the analysis target appearing in the one three-dimensional image after performing the first registration processing becomes small.

Description

Technical Field

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[0007]

[0001] The present invention relates to a three-dimensional image processing apparatus, a three-dimensional image processing method, and a program.

Background Art

[0002] The alveolar bone that supports teeth causes bone resorption (deficiency) due to periodontal disease, and if it progresses, there is a risk of eventually losing the teeth. Not only natural teeth, but also dental implants or orthopedic implants are said to be prone to bone resorption (deficiency) around the support part.

[0003] Conventionally, in the case of the alveolar bone, as a qualitative evaluation method of bone resorption, measurement of the depth of the periodontal pocket (groove between the tooth root and the gum) using a periodontal probe, examination of whether bleeding occurs due to the stimulation, and examination of the stability of the tooth using forceps have been performed.

[0004] However, it has been difficult to accurately know the state of the alveolar bone lost due to periodontal disease from the results of such examinations. In addition, these examination results were not easy for the patient himself / herself to understand the situation of his / her periodontal disease. Therefore, dental X-ray photographs of the teeth and their surroundings have been used to visually capture the shape of the alveolar bone. [[ID=​​​​​​​​​​​​​​​​​​​​​​ [Non-Patent Document 1] V Chappuis, O Engel, M Reyes, K Shahim, LP Nolte, D Buser, “Ridge alterations post-extraction in the esthetic zone: a 3D analysis with CBCT”, J. Dent. Res., Dec;92(12 Suppl):195S-201S. doi: 10.1177 / 0022034513506713. Epub 2013 Oct 24. PMID: 24158340; PMCID: PMC3860068 [Overview of the project] [Problems that the invention aims to solve]

[0008] In examinations using such medical 3D imaging, it is sometimes possible to examine changes compared to past results. By performing such comparisons, it is possible to make a more comprehensive and accurate assessment of bone resorption in the area of ​​interest, such as the progression of periodontal disease, compared to simply evaluating based on the results of a single scan.

[0009] However, these comparisons are generally visual and qualitative, and precise, quantitative comparisons at the pixel level, which would be possible by aligning the positions of the images, are not performed. As a result, sufficient information necessary for examination is not obtained, and the accuracy of the diagnosis is sometimes low. There is actually a reason for this: the morphological changes over time of teeth and periodontal tissues are often greater than those of hard tissues in other parts of the human body, making it difficult to perform the precise positioning necessary to compare 3D images taken at different times.

[0010] Despite these circumstances, it is desirable to obtain more quantitative information to further improve the accuracy of examinations and diagnoses in order to reduce the burden on time, money, and physical health. These circumstances apply not only to humans but also to animals. Furthermore, these circumstances also apply to dental implants and orthopedic implants, in addition to natural teeth.

[0011] In view of the above circumstances, the present invention aims to provide a technology that improves the accuracy of diagnosis using three-dimensional images. [Means for solving the problem]

[0012] One aspect of the present invention is a three-dimensional image processing apparatus for generating image data of a three-dimensional image used for analyzing a target for diagnosis, comprising an image processing unit that performs alignment of a three-dimensional image of the target for analysis captured at a first timing and a three-dimensional image of the target for analysis captured at a second timing different from the first timing, wherein the three-dimensional image captures an image of an object in a predetermined space including the target for analysis, the target for diagnosis is in the space, the target for analysis is a support part, and the alignment includes a first registration process that performs a rigid body transformation on one of the two three-dimensional images to reduce the difference between one and the other, and a second registration process that performs a rigid body transformation on one of the images to reduce the difference between the image of the target for analysis captured on the other and the image of the target for analysis captured on the one after the first registration process is performed.

[0013] One aspect of the present invention is a three-dimensional image processing method for generating image data of a three-dimensional image used for analyzing a target for diagnosis, comprising: an image processing step of performing alignment between a three-dimensional image of the target for analysis captured at a first timing and a three-dimensional image of the target for analysis captured at a second timing different from the first timing, wherein the three-dimensional image captures an image of an object in a predetermined space including the target for analysis, the target for diagnosis is in the space, the target for analysis is a support part, and the alignment includes: a first registration process of performing a rigid body transformation on one of the two three-dimensional images to reduce the difference between one and the other; and a second registration process of performing a rigid body transformation on one of the images to reduce the difference between the image of the target for analysis captured on the other and the image of the target for analysis captured on the one after the first registration process.

[0014] One aspect of the present invention is a program for causing a computer to function as the above-described three-dimensional image processing device. [Effects of the Invention]

[0015] This invention makes it possible to improve the accuracy of diagnosis using three-dimensional images. [Brief explanation of the drawing]

[0016] [Figure 1] An explanatory diagram illustrating the outline of the three-dimensional image processing device of the embodiment. [Figure 2] A figure showing an example of mask data in an embodiment. [Figure 3] A flowchart illustrating an example of a second registration process using mask data in the embodiment. [Figure 4] An example of experimental results evaluating the 3D alignment process in the embodiment. [Figure 5] A diagram showing an example of the hardware configuration of a 3D image processing device in an embodiment. [Figure 6]A diagram showing an example of the configuration of a control unit included in the three-dimensional image processing apparatus in the embodiment. [Figure 7] A flowchart showing an example of the processing flow executed by the three-dimensional image processing apparatus of the embodiment. [Figure 8] A diagram showing an example of the experimental results in the embodiment.

Mode for Carrying Out the Invention

[0017] (Embodiment) FIG. 1 is an explanatory diagram for explaining the outline of the three-dimensional image processing apparatus 1 of the embodiment. The three-dimensional image processing apparatus 1 generates image data of a three-dimensional image used for analyzing a diagnostic target. The diagnostic target is, for example, the tissue around the root of a natural tooth. The diagnostic target is, for example, the tissue around the fixture and abutment of a dental implant. The diagnostic target is, for example, the tissue around the holding part of an orthopedic implant.

[0018] Hereinafter, the root of a natural tooth, the fixture and abutment of a dental implant, and the holding part of an orthopedic implant are collectively referred to as a support part. If explained in terms of the support part, the diagnostic target is, for example, the tissue around the support part. The tissue around the support part as the diagnostic target is, for example, the tissue whose degree of contribution to the support of the support part is above a predetermined degree. Therefore, the tissue around the support part is, for example, the periodontal tissue. The tissue around the support part may be, for example, the alveolar bone or the femur.

[0019] <于000099>Note that a natural tooth is composed of a crown and a root. A dental implant is composed of an upper structure corresponding to the crown of a natural tooth, a fixture that supports the upper structure, and an abutment that connects the fixture and the upper structure. An orthopedic implant is composed of a head or plate that functions as a joint and a holding part including a stem or screw that supports the head or plate.

[0020] Hereinafter, the crown of a natural tooth, the upper structure of a dental implant, and the head or plate of an orthopedic implant are collectively referred to as a functional part.

[0021] Natural teeth may be human natural teeth or animal natural teeth. Dental implants may be human dental implants or animal dental implants. Orthopedic implants may be human orthopedic implants or animal orthopedic implants.

[0022] The 3D image processing device 1 includes a control unit 11. The details of the control unit 11 will be described later, but it includes a processor 91 such as a CPU (Central Processing Unit) and memory 92, and performs various processes through the operation of the processor 91 and memory 92.

[0023] The control unit 11 performs a 3D alignment process on the image to be analyzed and the image to be compared. The 3D alignment process is a process that aligns the positions of two 3D images to be performed. The images to be performed are the image to be analyzed and the image to be compared.

[0024] The image to be analyzed is a 3D image showing the object to be analyzed at a first timing. The comparison image is a 3D image showing the object to be analyzed at a second timing, which is different from the first timing. More specifically, the image to be analyzed is a 3D image of the object to be analyzed taken at the first timing, and the comparison image is a 3D image of the object to be analyzed taken at a second timing, which is different from the first timing.

[0025] More specifically, the comparison image is a 3D image that captures the result of capturing the object being analyzed at the second timing, as captured at the first timing, while the analysis image is a 3D image that captures the object being analyzed at the first timing. Conversely, the analysis image is a 3D image that captures the result of capturing the object being analyzed at the first timing, as captured at the second timing, while the comparison image is a 3D image that captures the object being analyzed at the second timing.

[0026] The second timing is, for example, earlier than the first timing. The second timing may also be later than the first timing. For simplicity, the following explanation will use the example where the first timing occurs after the second timing.

[0027] We have explained that the image to be analyzed and the comparison image are three-dimensional images that capture the image of the object to be analyzed. More specifically, the image to be analyzed and the comparison image capture images of objects within a predetermined space (hereinafter referred to as the "photographed space") that includes the object to be analyzed. The photographed space contains the object to be diagnosed. Therefore, the image to be analyzed and the comparison image capture images of the object to be diagnosed. More specifically, the object to be analyzed is the support part. The image to be analyzed and the comparison image may also capture images of functional parts. In other words, functional parts may exist in the photographed space. The photographed space is the so-called region of interest.

[0028] The imaging space may be a range defined according to a predetermined rule, or it may be a range defined by the user, for example. The predetermined rule may be any rule that includes the object of analysis and the object of diagnosis within the imaging space. For example, the predetermined rule may define the imaging space as a spherical space with a predetermined radius that encloses the object of diagnosis, with the object of analysis at its center.

[0029] An example of a prescribed rule is described for cases where there is a known bias in the morphology or positional relationship between the object to be analyzed and the object to be diagnosed. In such cases, the prescribed rule may be, for example, a rule in which the top surface is a square formed by sides of a predetermined length centered at a predetermined distance above the top of the object to be analyzed, and the imaging space is a rectangular prism with a predetermined height that encloses the object to be diagnosed.

[0030] As described above, the comparison image is a 3D image that captures the result of capturing the object to be analyzed at the second timing, and the analysis target image is a 3D image that captures the object to be analyzed at the first timing. Therefore, the person or animal with the object to be analyzed that appears in the analysis target image is the same as the person or animal with the object to be analyzed that appears in the comparison image.

[0031] For the sake of simplicity, the following explanation of the 3D image processing device 1 will use the example of a tooth root as the object of analysis.

[0032] Specifically, the 3D alignment process involves performing a rigid body transformation on the comparison image of the two images being analyzed, in order to minimize the difference between the first and second root images. The first root image is the image of the object being analyzed as it appears in the image being analyzed. The second root image is the image of the object being analyzed as it appears in the comparison image.

[0033] The process of applying a rigid body transformation to a comparison image to minimize the difference between the first and second root images is, for example, a transformation that increases the sum of the mutual information between pixel values ​​located at the same coordinates on the two images being processed.

[0034] As described above, both the analysis target image and the comparison target image, which are the subjects of the 3D alignment process, are images of the teeth of the same person or animal, but they are captured at different times. One of these two 3D images (i.e., the analysis target image and the comparison target image) is, for example, an image showing the analysis target and the surrounding teeth and alveolar bone. In this case, the other image is, for example, an image showing the analysis target and the surrounding teeth and alveolar bone of the same person or animal, but with some of the surrounding teeth missing due to extraction. The other image may be, for example, an image showing the analysis target and surrounding teeth of the same person or animal, and an image of the alveolar bone that is partially absorbed and missing.

[0035] By the way, the image to be analyzed and the image to be compared are, for example, images showing a portion of an image obtained by an imaging device such as an X-ray machine, and are images of the image that falls within the region of interest specified by the user. Hereinafter, the process of generating an image of a portion of such an image obtained by an imaging device such as an X-ray machine, which falls within the region of interest, based on the image obtained by the imaging device, will be referred to as the pre-image shaping process.

[0036] Regarding pre-image shaping processing, for example, the user may perform this processing using another computer before the image data of the image to be analyzed and the image data of the image to be compared are input to the 3D image processing device 1. The designation of the image to be analyzed may be performed by the control unit 11 after the image data of the image to be analyzed and the image data of the image to be compared are input to the 3D image processing device 1, in accordance with the user's instructions via the input unit 12, which will be described later.

[0037] When specifying the target of analysis, processing is performed according to the information entered by the user. Specifically, the user-entered information used when specifying the target of analysis is information that indicates a single point in the image, between the crown and root of the tooth to be analyzed.

[0038] Hereinafter, the information used in specifying the target of analysis, which indicates a single point in the image between the tooth root to be analyzed and the tooth crown containing the tooth, will be referred to as the first specification information. The single point between the tooth root to be analyzed and the tooth crown containing the tooth means a single point where the tooth root to be analyzed and the tooth crown containing the tooth can be distinguished. The purpose of this is to input information into the analysis device that, when specifying the maxilla, the tooth root position is above the specified point, and when specifying the mandible, the tooth root position is below the specified point.

[0039] In specifying the object to be analyzed, a region of predetermined shape and size is set as the region of interest, based on a point indicated by the first specified information. Therefore, in specifying the object to be analyzed, for example, a region of predetermined shape and size is set as the region of interest, centered on a point indicated by the first specified information.

[0040] The image data of the two images to be analyzed and the image data of the comparison image used for the 3D alignment process are, for example, image data obtained through pre-image shaping processing. Note that pre-image shaping processing is not necessarily required; images obtained directly from the imaging device may be used as the target for the 3D alignment process.

[0041] Hereafter, a pair of two 3D images on which the 3D alignment process is performed will be referred to as a target image pair. In other words, a target image pair is a pair of an image to be analyzed and an image to be compared.

[0042] For the sake of simplicity, the following explanation will describe the 3D image processing device 1 using the example where the image to be analyzed and the comparison image included in the target image set are images of the same person taken at different times, and the comparison image is an image taken before tooth extraction and the image to be analyzed is an image taken after tooth extraction.

[0043] It should be noted that there is not necessarily a drastic change between the two 3D images in the target image set, such as the loss of surrounding teeth due to tooth extraction. Regardless of whether or not a tooth has been extracted, if the subject to analysis is included in both images of the target image set, diagnosis is possible using the 3D image processing device 1. The diagnosis involves, for example, an analysis of morphological changes in the alveolar bone, etc., surrounding the subject to analysis.

[0044] Furthermore, the image data representing the image to be analyzed and the image data representing the comparison image satisfy the condition of identical size until the process of generating the 3D enhanced image is completed. The condition of identical size is that the size of each dimension of the image to be analyzed is the same as the size of each corresponding dimension of the comparison image.

[0045] In other words, the size-identical condition means that the dimensions (x, y, z) of the image being analyzed and the dimensions (x', y', z') of the image being compared are related by x=x', y=y', and z=z'. Thus, the size-identical condition means that each dimension of the three-dimensional image is identical in size to the corresponding dimension of the three-dimensional image being compared.

[0046] Note that x represents the size of the first dimension of the 3D image being analyzed. y represents the size of the second dimension of the 3D image being analyzed. z represents the size of the third dimension of the 3D image being analyzed. x' represents the size of the first dimension of the 3D image being compared. y' represents the size of the second dimension of the 3D image being compared. z represents the size of the third dimension of the 3D image being compared.

[0047] When the aforementioned pre-image shaping process is performed, the image to be analyzed and the comparison image subject to the 3D alignment process will satisfy the condition of having the same size. This is because the shape and size of the region of interest are predetermined as described above, and the pre-image shaping process sets a region of interest with the same shape and size regardless of the image.

[0048] Furthermore, regarding the size of the entire image in the XYZ directions, if the size of the image to be analyzed is matched to the region of interest, and the comparison image is made slightly larger than the image to be analyzed, and then cropped to match the image to be analyzed after the first registration, the occurrence of missing parts in the comparison image after the first registration can be further suppressed. This is because if the sizes are the same, parts that were originally outside the image will be included in the transformed image due to the angle transformation.

[0049] The 3D alignment process includes a first registration process and a second registration process. The first registration process is a rigid body transformation applied to the comparison image so that the overall differences between each image in the target image set are minimized. In other words, the first registration process is a rigid body transformation applied to one of the two 3D images, the analysis target image and the comparison image, so that the differences between one and the other are minimized. Hereinafter, the comparison image transformed by the first registration process will be referred to as the first transformed image.

[0050] In the first registration process, a point on the comparison image and a point on the analysis image may be specified. In this case, a rigid body transformation is performed on the comparison image, starting from a state where the specified points on each image coincide, so that the difference between the comparison image and the analysis image becomes smaller.

[0051] In addition, during the first registration, if the image sizes differ, a process may be performed to align the points between the tooth crown and tooth root specified by the user.

[0052] Hereinafter, the information used in the first registration process, which specifies a point on the comparison image and a point on the analysis image, will be referred to as the second designation information. The point indicated by the second designation information is, for example, a point between the tooth root being analyzed and the crown of the tooth containing the tooth being analyzed.

[0053] The second designation information is input by the user to the 3D image processing device 1, for example, via the input unit 12 described later. The control unit 11 acquires the second designation information input by the user and performs a rigid body transformation so that the difference between the comparison target image and the analysis target image is minimized, while ensuring that the points indicated by the acquired second designation information match.

[0054] The points indicated by the second designation information may be the same as those indicated by the first designation information. When the control unit 11 performs pre-image shaping processing, the first designation information is already input to the 3D image processing device 1 before the execution of the first registration processing. Therefore, in such cases, the first designation information may be used as the second designation information.

[0055] The second registration process is a process that performs a rigid body transformation on the first transformed image in such a way that the difference between the image of the object to be analyzed in the first transformed image and the image of the object to be analyzed in the original image is minimized.

[0056] The second registration process is a process that performs a transformation on the first transformed image to reduce the differences in the morphology of the images to be analyzed, based on information indicating the images of the objects to be analyzed in each image. The second registration process may also be a process that performs a rigid body transformation obtained according to a predetermined rule on the first transformed image. An example of such a process according to a predetermined rule is a process that uses mask data, which will be described later. An example of a second registration process using mask data will be described later.

[0057] <The significance of minimizing differences in tooth root morphology visible in images> Teeth and periodontal tissues undergo more drastic morphological changes over time compared to other parts of the human body. However, among teeth and periodontal tissues, the tooth root is a tissue that undergoes relatively little morphological change over time. Even with dental implants, the functional part may be replaced, the morphological changes of the surrounding bone are significant, and the morphological changes of the supporting part are minimal. In orthopedic implants, the morphological changes of the supporting part are less likely to occur compared to the morphological changes of the surrounding bone.

[0058] Therefore, by estimating changes in the tooth or periodontal tissue based on the position of the tooth root, it is possible to estimate changes in the tooth or periodontal tissue with higher accuracy compared to using other locations as a reference. Accordingly, by performing a process to reduce the morphological discrepancy between the image of the tooth root in the image to be analyzed and the image of the tooth root in the comparison image, the control unit 11 enables more accurate estimation of changes in the tooth or periodontal tissue. In the case of dental or orthopedic implants, it is also possible to estimate changes in the surrounding tissue with high accuracy by estimating changes in the surrounding tissue based on the position of the support.

[0059] <The significance of performing the first registration process> Performing the first registration process before the second registration process suppresses situations similar to overfitting in machine learning. Specifically, performing the first registration process before the second registration process prevents situations where the degree of agreement between the two images is high only around the root image indicated by the root specification information, and low for the image as a whole.

[0060] <Details of the second registration process using mask data> In the second registration process, mask data may be used. Mask data is data that indicates the pixels in the invariant root-containing region of the image to be analyzed (hereinafter referred to as "root-containing pixels"). The invariant root-containing region is a region on the image to be analyzed that encompasses the image being analyzed.

[0061] Mask data is, for example, image data of a binary image that satisfies the mask image conditions (hereinafter referred to as "mask image"). The mask image conditions are that the pixel values ​​of the invariant root-containing region are one of two predetermined pixel values, and the pixel values ​​of regions other than the invariant root-containing region are the other of two predetermined pixel values.

[0062] The two predetermined pixel values ​​are, for example, 0 and 1. The size of the mask image is the same as the image to be analyzed and the first transformed image. Therefore, the mask image is a three-dimensional image. The mask data may also be, for example, information indicating the boundary of the invariant root-containing region.

[0063] The mask data is, for example, binary image data in which the pixel values ​​of the image of an object in a predetermined space containing the object to be analyzed differ from the pixel values ​​of other images. The predetermined space containing the object to be analyzed is, for example, a space that is expanded in all directions by a predetermined width from the space that coincides with the image of the object to be analyzed.

[0064] Figure 2 shows an example of mask data in the embodiment. More specifically, Figure 2 shows an example of a mask image when the mask data in the embodiment is image data of the mask image. Figure 2 shows images M1, M2, and M3.

[0065] Image M1 is a view of the mask image from the direction of one of the three mutually orthogonal axes (hereinafter referred to as the "first axis direction"). Image M2 is a view of the mask image from the direction of the other of the three mutually orthogonal axes (hereinafter referred to as the "second axis direction"). Image M3 is a view of the mask image from the direction of one of the three mutually orthogonal axes, in the direction of a vector that is orthogonal to the vector parallel to the first axis direction and the vector parallel to the second axis direction (hereinafter referred to as the "third axis direction").

[0066] Point P in Figure 2 is an example of a point in the 3D image indicated by the second specified information.

[0067] Thus, the mask data is, in a sense, information indicating whether or not each pixel is in the invariant root-containing region. Therefore, for example, if the value of the root-containing pixels in the mask data is 1 and the values ​​of the other pixels are 0, multiplying each pixel of the image to be analyzed by the value of each pixel indicated by the mask data will yield an image that contains the image to be analyzed. Also, multiplying each pixel of the first transformed image by the value of each pixel indicated by the same mask data will yield an image that is highly likely to contain the image to be analyzed and has a high degree of agreement with the image obtained from the image to be analyzed.

[0068] However, since the mask data is obtained based on the image being analyzed, such an image is not necessarily obtained for the first transformed image. Therefore, if an appropriate rigid body transformation is performed on the first transformed image, the first transformed image will include the image of the target being analyzed, and thus an image with a high degree of agreement with the image obtained from the target image can be obtained.

[0069] In this way, the process of transforming the first transformed image to minimize the morphological differences of the image being analyzed is the second registration process using mask data. The second registration process using mask data will be explained further.

[0070] In the second registration process using mask data, a rigid body transformation is performed on the first transformed image to increase the degree of agreement between the partially analyzed image and the partially compared image. The partially analyzed image is an image of the invariant root-containing region of the analyzed image. More specifically, the partially analyzed image is a partial image of the analyzed image obtained based on the image data and mask data of the analyzed image, and is an image of the invariant root-containing region.

[0071] The partial comparison target image is an image obtained based on the image data and mask data of the first transformed image, and is an image of a region on the first transformed image that is captured within the candidate image extraction area. The candidate image extraction area is a region that satisfies the condition that if the image in which the region exists is not the first transformed image but the image to be analyzed, it is the tooth root-containing region.

[0072] Figure 3 is a flowchart illustrating an example of a second registration process using mask data in the embodiment. Specifically, the control unit 11 is responsible for executing each process described in Figure 3.

[0073] In the second registration process using mask data, first, only the mask region is extracted from the image to be analyzed to obtain a partially analyzed image (step S101). Next, in the second registration process using mask data, a rigid body transformation is performed on the first transformed image (step S102).

[0074] In the second registration process using mask data, a partial comparison image is then obtained from the first transformed image after the rigid body transformation (step S103). In the second registration process using mask data, the difference between the obtained partial analysis image and the partial comparison image is then obtained (step S104).

[0075] In the second registration process using mask data, it is then determined whether a predetermined termination condition regarding the smallness of the difference obtained in step S104 has been met (step S105). The predetermined termination condition may be, for example, a condition that the difference is smaller than a predetermined difference. The predetermined termination condition may also be, for example, a condition that the difference has converged to a size smaller than a predetermined difference.

[0076] The predetermined termination condition may be, for example, that the mutual information between the partially analyzed image and the partially compared image converges. Since the mutual information between the partially analyzed image and the partially compared image is a quantity that indicates the degree of agreement between the partially analyzed image and the partially compared image, convergence of the mutual information between the partially analyzed image and the partially compared image means that the differences converge.

[0077] If the termination condition is not met (step S105: NO), the rigid body transformation is updated according to a predetermined rule to reduce the difference between the partially analyzed image and the partially compared image (step S107).

[0078] Specifically, the parameter values ​​that determine the content of the rigid body transformation are updated according to predetermined rules so as to minimize the difference between the partially analyzed image and the partially compared image. After step S107, the process returns to step S102.

[0079] On the other hand, if the termination condition is met (step S105: YES), the first converted image obtained from the processing in the preceding step S102 is obtained as the result of the second registration process (step S106). The process ends after the execution of step S106.

[0080] In this way, the second registration process yields a first transformed image that satisfies the condition that the difference between the image of the object to be analyzed in the second transformed image and the image of the object to be analyzed in the target image is smaller than before the second registration process. More specifically, the second registration process yields a first transformed image in which the difference between the position or tilt of the image of the object to be analyzed in the first transformed image and the position or tilt of the image of the object to be analyzed in the target image is smaller than before the second registration process.

[0081] Hereinafter, the first transformed image obtained by executing the second registration process will be referred to as the second transformed image. Therefore, the image obtained as a result of the second registration process is the second transformed image.

[0082] The difference between the morphology of the image of the subject being analyzed in the original image and the morphology of the image of the subject being analyzed in the second-transformed image is smaller than the difference between the morphology of the image of the subject being analyzed in the original image and the morphology of the image of the subject being analyzed in the first-transformed image. Since the morphology of supporting parts such as tooth roots changes over time less than that of other periodontal tissues, it is possible to estimate the aging changes in the state of the tooth or periodontal tissue that occurred between the original image and the second-transformed image with higher accuracy by using the original image and the second-transformed image.

[0083] <Experimental Results> Figure 4 shows an example of experimental results evaluating the 3D alignment process in the embodiment. Figure 4 shows images G1-1, G1-2, G1-3, G2-1, G2-2, G2-3, G3-1, G3-2, G3-3, G4-1, G4-2, and G4-3.

[0084] Image G1-1 is a view of the pre-transformation target image from the first axis direction. The pre-transformation target image is the comparison image before the 3D alignment process is performed. Image G1-2 is a view of the pre-transformation target image from the second axis direction. Image G1-3 is a view of the pre-transformation target image from the third axis direction.

[0085] Image G2-1 is a view of the first transformed image from the first axis direction. Image G2-2 is a view of the first transformed image from the second axis direction. Image G2-3 is a view of the first transformed image from the third axis direction. Image G3-1 is a view of the second transformed image from the first axis direction. Image G3-2 is a view of the second transformed image from the second axis direction. Image G3-3 is a view of the second transformed image from the third axis direction.

[0086] Image G4-1 is a view of the image to be analyzed from the first axis direction. Image G4-2 is a view of the image to be analyzed from the second axis direction. Image G4-3 is a view of the image to be analyzed from the third axis direction.

[0087] Figure 4 shows that the first transformed target image differs less from the original target image in terms of the position and orientation of the object being analyzed, and the second transformed image differs less from the original target image in terms of the position and orientation of the object being analyzed, compared to the first transformed image. Thus, Figure 4 demonstrates that 3D alignment processing can produce a comparison image in which the position and orientation of the object being analyzed differs less from the original target image.

[0088] Thus, 3D alignment is a process of aligning two 3D images.

[0089] Both the image to be analyzed and the second-transformed image are three-dimensional images. Therefore, it is possible to generate a three-dimensional image (hereinafter referred to as the "three-dimensional highlighting image") in which the differences between the image to be analyzed and the second-transformed image are highlighted by coloring or other means. The process of generating such a three-dimensional highlighting image (hereinafter referred to as the "three-dimensional highlighting image generation process") is performed, for example, by the control unit 11.

[0090] Once a 3D enhanced image is generated, users can visually understand the difference between the condition of the teeth or periodontal tissue shown in the image being analyzed and the condition of the teeth or periodontal tissue shown in the comparison image.

[0091] The generation of 3D enhanced images is beneficial not only for medical professionals but also for patients who are not medical professionals. This is because periodontal tissue examination charts are a series of numbers, requiring knowledge of what each number means, but 3D images do not require much such knowledge, making it easier to understand the examination results.

[0092] The 3D highlighting image may also be a 3D image that shows differences using different colors depending on whether the difference is positive or negative.

[0093] The 3D enhanced image may, for example, be an image that shows whether a tooth root belongs to the first, second, or third root region. The first root region is a part of the tooth root that is not covered by bone at the first time point. The second root region is a part of the tooth root that was covered by bone at the first time point but is not covered by bone at the second time point, which is later than the first time point. The third root region is a part of the tooth root that is covered by bone at both the first and second time points.

[0094] In such a three-dimensional enhanced image, the first root region is represented, for example, in white; the second root region in, for example, in red; and the third root region in, for example, in green.

[0095] Hereafter, the teeth or periodontal tissue visible in the comparison image will be referred to as the first imaged tissue. Hereafter, the teeth or periodontal tissue visible in the image to be analyzed will be referred to as the second imaged tissue.

[0096] Both the image to be analyzed and the second transformed image are three-dimensional images. Therefore, both the image to be analyzed and the second transformed image are sets of pixel values. Since pixels are ordered sets, quantitative information about the image to be analyzed and the second transformed image can also be obtained based on the images to be analyzed and the second transformed image. Hereinafter, the process of obtaining quantitative information about the image to be analyzed and the second transformed image (hereinafter referred to as "quantitative information") will be called the quantitative information acquisition process. The quantitative information acquisition process is performed, for example, by the control unit 11.

[0097] Quantitative information regarding the image under analysis and the second-processed image includes, for example, information that numerically indicates the difference between the image under analysis and the second-processed image. Examples of such numerical information include information indicating the amount of bone resorption in the second tissue relative to the first tissue. Another example of such numerical information is information indicating the amount of bone proliferation in the second tissue relative to the first tissue.

[0098] The information that numerically represents the difference between the image to be analyzed and the image after the second transformation may, for example, be information indicating the three-dimensional volume of each of the first, second, and third root regions mentioned above.

[0099] Thus, using the second transformed image allows for obtaining quantitative information with higher accuracy than using the comparison image before the 3D alignment process. Therefore, the 3D image processing device 1 that obtains the second transformed image can improve the accuracy of diagnosing the condition of teeth or periodontal tissues.

[0100] <Regarding the generation of mask data> Here, we will explain a specific example of mask data generation. Specifically, the generation of mask data is performed by a computer. For example, the control unit 11 performs the generation of mask data. Note that the generation of mask data does not necessarily have to be performed by the 3D image processing device 1, but may be performed by other devices.

[0101] In such cases, the 3D image processing device 1 acquires the mask data generated by another device that generated the mask data before executing the 3D alignment process and uses it in the 3D alignment process.

[0102] For the sake of simplicity, the following example of the mask data generation process (hereinafter referred to as "mask data generation process") will be explained using the case where the control unit 11 performs the process as an example.

[0103] As mentioned above, the image to be analyzed is a three-dimensional image. Therefore, the control unit 11 can perform processing on the three-dimensional image as a set of two-dimensional images. In the mask data generation process, the control unit 11 processes the image to be analyzed as an ordered set in which the slice images to be analyzed are elements, and the order of the elements is ordered in the direction from the analysis target toward the crown of the tooth containing the analysis target (hereinafter referred to as the "analysis target ordered set"). For the sake of simplicity, the direction from the analysis target toward the crown of the tooth containing the analysis target will be referred to as the direction from the tooth root toward the crown of the analysis target.

[0104] The slice images to be analyzed are two-dimensional images resulting from slicing the target image in the direction from the tooth root towards the tooth crown. Therefore, the slice images to be analyzed are a type of so-called slice image.

[0105] The ranking of the ordered set may be higher from the root to the crown of the tooth being analyzed, or lower from the crown to the root of the tooth being analyzed, but one of these rules applies. The information regarding the direction from the root to the crown of the tooth being analyzed in mask data processing is obtained, for example, based on point position information and jaw designation information. Point position information is information that indicates a single point in the image between the crown and root of the tooth being analyzed. The first and second designation information described above are both examples of point position information.

[0106] Jaw designation information indicates whether the target of analysis is the upper jaw or the lower jaw in the 3D image. Jaw designation information is input to the 3D image processing device 1 by the user, for example, via the input unit 12. In such cases, the control unit 11 acquires the input jaw designation information. Point position information is input to the 3D image processing device 1 by the user, for example, via the input unit 12. In such cases, the control unit 11 acquires the input point position information.

[0107] In the mask data generation process, the control unit 11 selects whether each slice image to be analyzed is a 2D image for mask data generation. A 2D image for mask data generation is a slice image to be analyzed in which the difference in first rank is greater than the difference in second rank, and in which the difference in first rank is greater than the absolute value of the difference in rank between the slice image containing the point indicated by the point specification information and the tooth crown boundary image.

[0108] The first rank difference is the absolute value of the rank difference between the image and the coronal boundary image. The second rank difference is the absolute value of the rank difference between the image and the root-encompassing boundary image.

[0109] A crown boundary image is one of the slice images to be analyzed that captures the image of the tooth crown (hereinafter referred to as "crown image") and satisfies certain conditions. A root boundary image is one of the slice images to be analyzed that captures the image of the tooth root (hereinafter referred to as "root boundary image") and satisfies certain conditions.

[0110] The predetermined conditions that a tooth crown boundary image must satisfy are, for example, that the difference in rank between it and a tooth root boundary image is smaller than that of other tooth crown images. The predetermined conditions that a tooth root boundary image must satisfy are, for example, that the difference in rank between it and a tooth crown boundary image is smaller than that of other tooth root boundary images. Therefore, a tooth crown image may be, for example, an analysis slice image that shows the image of the tooth crown but not the image of the tooth root. A tooth root boundary image may be, for example, an analysis slice image that shows the image of the tooth root but not the image of the tooth crown.

[0111] In this way, the control unit 11 obtains a three-dimensional image (hereinafter referred to as the "three-dimensional image for mask data generation") which includes the tooth root to be analyzed but does not include the tooth crown to be analyzed, as a set of two-dimensional images for mask data generation. Next, the control unit 11 performs a binarization process to convert the three-dimensional image for mask data generation into a binary image in which the pixel values ​​of the tooth image and the other images are different.

[0112] The binarization process includes, for example, arc-connection point determination processing. Arc-connection point determination processing is the process of determining that the set of other ends of curves in the 3D image used for mask data generation that satisfy the arc-connection condition, with one end being a curve indicated by point position information (hereinafter referred to as "mask curve"), is a tooth image. In other words, arc-connection point determination processing is the process of determining pixels in the image to be executed that are located at the other end of curves that satisfy the arc-connection condition (hereinafter referred to as "arc-connected pixels").

[0113] The arc-connection condition is that the difference between the pixel values ​​of all points on the mask curve and the pixel values ​​of the points indicated by the point position information is within a predetermined range.

[0114] The binarization process, which includes arc-connection determination processing, also includes a setting process. The setting process involves setting one of two predetermined pixel values ​​to the pixel value of an arc-connected pixel, and setting the other to the pixel value of a pixel that is not an arc-connected pixel. Furthermore, the control unit 11 can reduce the possibility that arc-connected pixels include areas other than tooth roots, such as bone regions, by executing the arc-connection determination processing in a way that satisfies subconditions.

[0115] A secondary condition is that the range of the arc-connected pixels is smaller than the actual tooth root. The control unit 11 may perform a process to expand the range of the arc-connected pixels by morphological transformation after the binarization process. By performing a process to expand the range of the arc-connected pixels by morphological transformation after the binarization process, the control unit 11 can generate mask data that includes a safety margin around the tooth root.

[0116] Through this binarization process, the 3D image used for generating mask data is converted into a binary image where the pixel values ​​of the tooth image and the rest of the image are different. The image data of the binarized 3D image used for generating mask data after conversion is an example of mask data.

[0117] Figure 5 shows an example of the hardware configuration of the 3D image processing device 1 in an embodiment. The 3D image processing device 1 includes a control unit 11 which has a processor 91 such as a CPU (Central Processing Unit) and memory 92 connected by a bus, and executes a program. The 3D image processing device 1 functions as a device comprising the control unit 11, input unit 12, communication unit 13, storage unit 14 and output unit 15 through the execution of the program.

[0118] More specifically, the processor 91 reads the program stored in the storage unit 14 and stores the read program in the memory 92. By executing the program stored in the memory 92, the processor 91 functions as a device comprising a control unit 11, an input unit 12, a communication unit 13, a storage unit 14, and an output unit 15.

[0119] The control unit 11 controls the operation of various functional units of the 3D image processing device 1. The control unit 11 may, for example, perform 3D alignment processing. The control unit 11 may, for example, perform mask data generation processing. The control unit 11 may, for example, perform 3D highlighting image generation processing. The control unit 11 may, for example, perform quantitative information acquisition processing. The control unit 11 may, for example, perform pre-image shaping processing.

[0120] The input unit 12 includes input devices such as a mouse, keyboard, or touch panel. The input unit 12 may also be configured as an interface for connecting these input devices to the 3D image processing device 1. The input unit 12 receives various types of information for input to the 3D image processing device 1.

[0121] The input unit 12 receives information such as instructions to the control unit 11 by the user. For example, first designation information may be input to the input unit 12. For example, second designation information may be input to the input unit 12. For example, jaw designation information may be input to the input unit 12. For example, point position information may be input to the input unit 12.

[0122] The communication unit 13 is configured to include a communication interface for connecting the 3D image processing device 1 to an external device. The communication unit 13 communicates with the external device via wired or wireless connection. The external device is, for example, the device that transmits the image to be analyzed. The communication unit 13 acquires the image to be analyzed by communicating with the device that transmits the image to be analyzed. The external device is, for example, the device that transmits the image to be compared. The communication unit 13 acquires the image to be compared by communicating with the device that transmits the image to be compared.

[0123] The source device for transmitting the image to be analyzed and the comparison image may be the same. In such a case, the source device for transmitting the image to be analyzed and the comparison image may, for example, perform pre-image shaping processing. In such a case, the image to be analyzed and the comparison image transmitted by the source device for transmitting the image to be analyzed and the comparison image are the image to be analyzed and the comparison image obtained through pre-image shaping processing.

[0124] The external device may be, for example, a device to which the image data of the second converted image is output. In such a case, the communication unit 13 communicates with the device to which the image data of the second converted image is output and outputs the image data of the second converted image to the device to which the image data of the second converted image is output.

[0125] The external device may be, for example, a device to which the image data of the 3D highlighting image is output. In such a case, the communication unit 13 communicates with the device to which the image data of the 3D highlighting image is output and outputs the image data of the 3D highlighting image to the device to which the image data of the 3D highlighting image is output.

[0126] The external device may be, for example, a device to which quantitative information is output. In such a case, the communication unit 13 communicates with the device to which quantitative information is output and outputs quantitative information to the device to which quantitative information is output.

[0127] The external device may be, for example, a device that generates the mask data. In such a case, the communication unit 13 acquires the mask data by communicating with the device that generates the mask data. The device that generates the mask data is a device that acquires the analysis target image to be subjected to 3D alignment processing, and generates mask data by executing a mask data generation process based on the acquired analysis target image.

[0128] The storage unit 14 is configured using a computer-readable storage medium such as a magnetic hard disk drive or a semiconductor storage device. The storage unit 14 stores various information related to the 3D image processing device 1. The storage unit 14 stores information input via, for example, the input unit 12 or the communication unit 13. The storage unit 14 stores, for example, a comparison target image. The comparison target image may be obtained from an external device, but it may also be pre-stored in the storage unit 14. The storage unit 14 may also store mask data.

[0129] The output unit 15 outputs various types of information. The output unit 15 is comprised of a display device such as a CRT (Cathode Ray Tube) display, a liquid crystal display, or an organic EL (Electro-Luminescence) display. The output unit 15 may also be configured as an interface for connecting these display devices to the 3D image processing device 1. The output unit 15 outputs information input to, for example, the input unit 12 or the communication unit 13.

[0130] The output unit 15 may, for example, display the second transformed image. The output unit 15 may, for example, display the image to be analyzed. The output unit 15 may, for example, display a 3D enhanced image. The output unit 15 may, for example, display quantitative information.

[0131] Figure 6 shows an example of the configuration of the control unit 11 of the 3D image processing device 1 in the embodiment. The control unit 11 comprises an image processing unit 111, an input control unit 112, a communication control unit 113, a storage control unit 114, and an output control unit 115.

[0132] The image processing unit 111 performs at least a three-dimensional alignment process. The image processing unit 111 may, for example, perform a three-dimensional highlighting image generation process. The image processing unit 111 may, for example, perform a quantitative information acquisition process. The image processing unit 111 may, for example, perform a pre-image shaping process. The image processing unit 111 may, for example, perform a mask data generation process.

[0133] The image processing unit 111 may, for example, control the operation of the communication control unit 113 to cause the communication unit 13 to output the image data of the second converted image to the output device for the image data of the second converted image. In such a case, the image processing unit 111 outputs the image data of the second converted image to the communication control unit 113. The communication control unit 113 then outputs the acquired image data to the communication unit 13.

[0134] The image processing unit 111 may, for example, control the operation of the output control unit 115 to output the image data of the second converted image to the output unit 15. In this case, the image processing unit 111 outputs the image data of the second converted image to the output control unit 115. The output control unit 115 outputs the acquired image data to the output unit 15.

[0135] The input control unit 112 controls the operation of the input unit 12. The communication control unit 113 controls the operation of the communication unit 13. The memory control unit 114 controls the operation of the memory unit 14.

[0136] The output control unit 115 controls the operation of the output unit 15. For example, the output control unit 115 controls the operation of the output unit 15 to display the image to be analyzed on the output unit 15. For example, the output control unit 115 controls the operation of the output unit 15 to display the second converted image obtained by the image processing unit 111 on the output unit 15.

[0137] The output control unit 115 may, for example, when the image processing unit 111 performs a 3D highlighting image generation process, control the operation of the output unit 15 to display the 3D highlighting image obtained by the image processing unit 111 on the output unit 15. The output control unit 115 may, for example, when the image processing unit 111 performs a quantitative information acquisition process, control the operation of the output unit 15 to display the quantitative information obtained by the image processing unit 111 on the output unit 15.

[0138] An example of the processing flow performed by the 3D image processing device 1 is explained using Figure 7 below. For simplicity, Figure 7 illustrates an example of processing where the second specified information has already been entered by the user, and the analysis target image and comparison target image, which have already undergone pre-image shaping processing, are used.

[0139] Figure 7 is a flowchart showing an example of the processing flow performed by the three-dimensional image processing device 1 of the embodiment. The target image set is input to the input unit 12 or the communication unit 13 (step S201). Next, the image processing unit 111 performs a first registration process on the images of the input target image set (step S202). Next, the image processing unit 111 performs a second registration process (step S203).

[0140] Next, the image processing unit 111 controls the operation of the communication control unit 113 or the output control unit 115 to output the image data of the second converted image to the output destination corresponding to each controlled object (step S204). Therefore, in step S204, when the image processing unit 111 controls the operation of the communication control unit 113, it controls the operation of the communication unit 13 through the control of the operation of the communication control unit 113 to output the image data of the second converted image to the output destination device. In this case, as described above, the image processing unit 111 outputs the image data of the second converted image to the communication control unit 113.

[0141] In step S204, if the image processing unit 111 controls the operation of the output control unit 115, it controls the operation of the output control unit 115 to output the image data of the second converted image to the output unit 15. In this case, as described above, the image processing unit 111 outputs the image data of the second converted image to the output control unit 115.

[0142] (Experimental results) An example of experimental results using the 3D image processing device 1 is shown. Figure 8 is a diagram showing an example of experimental results in an embodiment. The experiment involved obtaining images showing changes in the patient's alveolar bone using the 3D image processing device 1. In the experiment, images showing changes in the alveolar bone were obtained based on images of the patient's teeth taken in 2018 and images of the patient's teeth taken in 2020. Image G5-1 highlights the changes in the alveolar bone. For example, region A1 in image G5-1 shows changes in the alveolar bone. In image G5-2, the upper left, upper right, and lower left images are examples of cross-sections of 3D images taken in 2018.

[0143] Image G5-3 is an example of a 3D image taken in 2018. Image G5-4 shows examples of cross-sections of 3D images taken in 2020, with the upper left, upper right, and lower left images being examples. Image G5-5 is an example of a 3D image taken in 2020. The difference between the image of region A2 in Image G5-3 and the image of region A3 in Image G5-5 is region A1. More specifically, region A1 indicates that alveolar bone resorption occurred between 2018 and 2020. According to Image G5-1, the amount of alveolar bone resorption in region A1 between 2018 and 2020 was 6.9 cubic millimeters.

[0144] In the three-dimensional image processing device 1 configured in this way, a rigid body transformation is performed on one of the three-dimensional images to reduce the difference between the images of the object to be analyzed captured in two three-dimensional images taken at different timings. As mentioned above, tooth roots are less prone to morphological changes over time compared to teeth and other periodontal tissues. Therefore, such a three-dimensional image processing device 1 can improve the accuracy of diagnosing the condition of teeth or periodontal tissues.

[0145] (modified version) Furthermore, if point position information and jaw information are input to the 3D image processing device 1, the image processing unit 111 may execute a separation-enhancing image generation process. The separation-enhancing image generation process is a process that generates an image in which the degree of separation between the tooth image and the alveolar bone image in the analysis target image and the second transformed image is increased. For the sake of simplicity in the following explanation, the image on which the separation-enhancing image generation process is executed will be called the separation target image. The separation target image is either the analysis target image or the second transformed image.

[0146] <About image generation processing to improve separation accuracy> Image generation processing to improve separation accuracy is an example of mask data generation processing. In the image generation process for improving separation accuracy, a first sub-separation process is executed. The first sub-separation process selects slices from the slice images resulting from slicing the target image in the direction from the tooth root to the tooth crown, based on jaw information, that are closer to the tooth root than the position indicated by the point position information. The determination of whether or not a slice image is closer to the tooth root is made based on the point position information and jaw information, as described above.

[0147] The process of selecting a slice closer to the tooth root than the position indicated by the point position information in the resolution-enhancing image generation process is, for example, the process of selecting whether or not each slice image to be analyzed is a 2D image for generating mask data, as described above.

[0148] When the image to be separated is a CT image, the image to be separated is what is known as an axial image. Therefore, in such cases, the long axis of the tooth is approximately perpendicular to the slice image. When analyzing a tooth that is significantly tilted, the slice image may be a slice image that has been recut with a slice perpendicular to the tooth axis, with the user specifying the tooth axis or cervical line.

[0149] In the image generation process for improving separation accuracy, a second sub-separation process is then performed. The second sub-separation process is a process that binarizes the image to be separated using a threshold value greater than or equal to a predetermined value. The predetermined value is a value that satisfies the condition that the region on the image to be separated that the image processing unit 111 determines to be a pixel representing a tooth is relatively small even when the threshold value is less than the predetermined value. The second sub-separation process yields a binary image in which, for example, the pixel values ​​of teeth and alveolar bone are 1, and the pixel values ​​of all other pixels are 0.

[0150] In the image generation process for improving separation accuracy, the third sub-separation process is executed next. The third sub-separation process is the arc-shaped connection point determination process.

[0151] In the image generation process for improving separation, the fourth sub-separation process is performed next. The fourth sub-separation process replaces the pixel values ​​of pixels that are surrounded by arc-connected pixels but are not arc-connected pixels with the pixel values ​​of arc-connected pixels.

[0152] In the image generation process for improving separation accuracy, the fifth sub-separation process is performed next. The fifth sub-separation process is a process in which the region of arc-shaped connected pixels, which was generated smaller than the actual root outline in the second sub-separation process to reliably separate the tooth root and alveolar bone, is enlarged to a size larger than the actual root outline by morphological transformation.

[0153] In the image generation process for improving separation accuracy, the sixth sub-separation process is executed next. The sixth sub-separation process sets all pixel values ​​of the slice images that were not selected in the first sub-separation process to 0.

[0154] Thus, the separation-enhancing image generation process involves performing arc-shaped connection point determination based on the position of a point, setting it to be smaller than the actual tooth root, and then enlarging it. As a result, it becomes possible to perform a second registration, and a mask can be automatically generated from only the first designation information and jaw designation information. Therefore, by performing the separation-enhancing image generation process, an image is generated in which the degree of separation between the tooth and alveolar bone in the target image is increased.

[0155] Up to this point, the explanation has been based on the example of a user inputting the first designation information, second designation information, point position information, and jaw designation information. However, the first designation information, second designation information, point position information, and jaw designation information may be pre-stored in the storage unit 14. For example, if the user has selected the image to be analyzed and the image to be compared, and the position indicated by the first designation information is approximately the same for all images, then the user does not need to input the first designation information.

[0156] Furthermore, even without user selection, if the position indicated by the first designation information is approximately the same for all images due to limitations imposed by the shooting environment, the user does not need to input the first designation information. The same applies to the second designation information, point position information, and jaw designation information.

[0157] Furthermore, in generating mask data, range position information may be used instead of point position information. Range position information may also be information indicating the cervical region of the tooth. Since the tooth root is below the cervical region, mask data can be generated even if range position information is used instead of point position information.

[0158] Furthermore, the 3D image processing device 1 does not necessarily have to consist of a single enclosure. The 3D image processing device 1 may be implemented using multiple information processing devices that are connected to each other via a network. In this case, each functional unit of the 3D image processing device 1 may be distributed and implemented across multiple information processing devices.

[0159] The output unit 15 is an example of a predetermined display destination. The first converted image is one example of the result after the execution of the first registration process.

[0160] Furthermore, all or part of the functions of the 3D image processing device 1 may be implemented using hardware such as an ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), or FPGA (Field Programmable Gate Array). The program may be recorded on a computer-readable recording medium. Computer-readable recording media include, for example, portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, and storage devices such as hard disks built into computer systems. The program may also be transmitted via a telecommunications line.

[0161] Although embodiments of this invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and includes designs and the like that do not depart from the spirit of this invention. [Explanation of Symbols]

[0162] 1...3D image processing unit, 11...control unit, 12...input unit, 13...communication unit, 14...storage unit, 15...output unit, 111...image processing unit, 112...input control unit, 113...communication control unit, 114...storage control unit, 115...output control unit, 91...processor, 92...memory

Claims

1. A three-dimensional image processing device that generates image data of a three-dimensional image used for the analysis of a target for diagnosis, An image processing unit that performs alignment of a three-dimensional image of the object to be analyzed, taken at a first timing, and a three-dimensional image of the object to be analyzed, taken at a second timing different from the first timing. Equipped with, The aforementioned three-dimensional image captures an image of an object in a predetermined space that includes the object to be analyzed. The object to be diagnosed is located within the aforementioned space. The object of analysis is the support part, The aforementioned alignment is, A first registration process that performs a rigid body transformation on one of the two three-dimensional images in order to reduce the difference between one and the other, A second registration process is performed to perform a rigid body transformation on the one such that the difference between the image of the object to be analyzed projected onto the other and the image of the object to be analyzed projected onto the one after the execution of the first registration process is minimized. including, Three-dimensional image processing device.

2. In the second registration process, a mask data is used that indicates pixels from the other pixel that represent a region on the other three-dimensional image that includes the image to be analyzed. The three-dimensional image processing apparatus according to claim 1.

3. The aforementioned mask data is image data of a binary image where the pixel values ​​of the image of an object in a predetermined space containing the object to be analyzed differ from the pixel values ​​of other images. The three-dimensional image processing apparatus according to claim 2.

4. The binary image is a three-dimensional image that includes an image of the support portion to be analyzed, but does not include an image of the functional portion of the tooth having the support portion to be analyzed, and the other end of the curve is a curve with one end being a predetermined point between the image of the support portion and the image of the functional portion, wherein the pixel value of an arc-connected pixel located at the other end of the curve satisfies the condition that the difference between the pixel values ​​of all points on the curve and the pixel value of the predetermined point is within a predetermined range, and the pixel value of a pixel that is not an arc-connected pixel is the other of the two predetermined pixel values, The functional part is a crown of a natural tooth, a superstructure of a dental implant, or a head or plate of an orthopedic implant. The three-dimensional image processing apparatus according to claim 3.

5. An output control unit that controls the operation of a predetermined display destination. Equipped with, The image processing unit generates a three-dimensional highlighting image, which is a three-dimensional image in which the differences between the other and the one are highlighted, based on the other and the converted one obtained by the alignment. The output control unit causes the three-dimensional enhanced image to be displayed at the display destination. The three-dimensional image processing apparatus according to claim 1.

6. An output control unit that controls the operation of a predetermined display destination. Equipped with, The image processing unit acquires quantitative information that numerically represents the difference between the other and the one, based on the other and the converted one obtained by the alignment. The output control unit causes the quantitative information to be displayed on the display destination. The three-dimensional image processing apparatus according to claim 1.

7. A three-dimensional image processing method for generating image data of a three-dimensional image used for the analysis of a target for diagnosis, Image processing step of performing alignment between a three-dimensional image of the object to be analyzed, taken at a first timing, and a three-dimensional image of the object to be analyzed, taken at a second timing different from the first timing. It has, The aforementioned three-dimensional image captures an image of an object in a predetermined space that includes the object to be analyzed. The object to be diagnosed is located within the aforementioned space. The object of analysis is the support part, The aforementioned alignment is, A first registration process that performs a rigid body transformation on one of the two three-dimensional images in order to reduce the difference between one and the other, A second registration process is performed to perform a rigid body transformation on the one such that the difference between the image of the support portion projected onto the other and the image of the support portion projected onto the one after the execution of the first registration process is minimized. including, Three-dimensional image processing methods.

8. A program for causing a computer to function as a three-dimensional image processing device according to claim 1.

Images