KR20200134037A - Automatic generation method of neural tube line and medical image processing apparatus therefor - Google Patents

Abstract

Disclosed are a method for automatically generating a neural tube line and a medical image processing device therefor. According to an embodiment of the present invention, the method for automatically generating a neural tube comprises the steps of: acquiring a panoramic image and a CT cross-sectional image; searching for a small hole location using distance information of a hard tissue within the CT cross-sectional image constituting a curve line of the panoramic image; searching for an initial position of a neural tube using profile information of the CT cross-sectional image; and generating a neural tube line using the searched small hole location and the initial position.

Description

Automatic generation method of neural tube line and medical image processing apparatus therefor

The present invention relates to a medical image processing technology for neural tube search.

In the implant surgery process using software, after establishing an implant placement plan in consideration of the implant structure (crown, abutment, fixture, etc.) and the patient's anatomical structure on the software, implant surgery is performed. Particularly, in the case of planning implant placement among surgical cases, a virtual neural tube is created from the image so that nerve damage does not occur, and this is confirmed. In the currently commercialized medical imaging program, when a user directly inputs feature points from an acquired tooth image, a neural tube is created by connecting each feature point with a line.

The manual neural tube generation method by the user takes a lot of time because the user has to find and input the appropriate feature points in the tooth image, and trial and error of selecting unintended feature points may occur for users who are not good at handling manipulation means such as a mouse. I can. Furthermore, it is inefficient because the user must repeat the manipulation operation to find appropriate feature points in the tooth image every time a neural tube is created. In other words, since it takes a lot of time and hassle to create a neural tube, it soon acts as a factor that delays the overall digital design process and reduces efficiency.

According to an embodiment, a method for automatically generating a neural tube line capable of minimizing user manipulation by automatically generating a neural tube line from a medical image and a medical image processing apparatus therefor is proposed.

The method for automatically generating a neural tube according to an embodiment includes the steps of acquiring a panoramic image and a CT cross-sectional image, and a small hole location using distance information between a hard tissue within a CT cross-sectional image constituting a curve line of the panoramic image. And searching for, searching for an initial position of the neural tube using profile information of the CT cross-sectional image, and generating a neural tube line using the searched pore position and the initial position.

The step of searching for a small hole location includes obtaining a first CT cross-sectional image corresponding to a center position of a curve line of a panoramic image, and a distance difference between the edge position of the acquired first CT cross-sectional image and a hard tissue start point It may include the step of searching for a small hole by using and generating a first control point from the searched small hole.

The step of generating the first control point includes the steps of searching for a lowermost position of the jawbone in the obtained first CT cross-sectional image, and the jawbone from the edge position of the first CT cross-sectional image sequentially while rising upward based on the lowermost position of the jawbone. Calculating the line distance by moving the pixel horizontally to the starting point; determining the point as a small hole if the distance difference between the previous line distance and the current line distance is more than a preset distance according to the distance calculation result; and It may include a system for generating a first control point for generating an end point of the line.

The method of automatically generating a neural tube may further include searching for a small hole in the moved CT cross-sectional image while moving the CT cross-sectional image when the small hole search fails.

The step of searching for the initial position of the neural tube includes obtaining a second CT cross-sectional image corresponding to the edge position of the curve line of the panoramic image, and moving a pixel horizontally in the acquired second CT cross-sectional image. Generating a line profile graph for the line data, and generating a second control point from the searched neural tube center point by searching for a center point of a neural tube through analysis of a change shape of an inflection point of the generated line profile graph. .

The step of generating the line profile graph includes the steps of searching for the lowermost position of the jawbone at the bottom of the acquired second CT cross-sectional image, and setting the searched lowermost position of the jawbone as a starting point, and moving up one by one based on the starting point. A step of generating horizontal lines of and constructing a line profile graph indicating an intensity value for each pixel for each of the generated horizontal line pixel data.

The step of generating the second control point includes searching for an inflection point corresponding to the jawbone and neural tube epidermis data among the inflection points formed in the line profile graph based on the intensity value, and the center of the neural tube passing through the retrieved jawbone and neural tube epidermis data. It may include the step of searching for an inflection point corresponding to the point and generating a second control point from the searched inflection point.

The step of retrieving the initial position of the neural tube may further include reconstructing the line profile graph using B-Spline interpolation to analyze the shape of the inflection point change.

In the step of searching for the initial position of the neural tube, if the center point of the neural tube is not found through analysis of the shape of the inflection point change, it moves to the next CT section image to the left or right to create a line profile graph and analyze the shape of the change in the inflection point in the generated line profile graph. Thus, it may further include the step of searching for the center point of the neural tube.

In the step of searching for the initial position of the neural tube, if the center point of the neural tube is not found through analysis of the shape of the change of the inflection point, the step of searching for the lowermost position of the jawbone at the bottom of the second CT section image, and the pixel upward by a preset jawbone thickness from the searched lowermost position of the jawbone. The step of moving, and generating a temporary control point from the corresponding point by moving the pixel upward by a preset neural tube position from a point moved upward by a preset jawbone thickness.

The step of generating the neural tube line includes terminating the search for the inflection point if the distance between the inflection point and the initially searched pore is within a preset distance through analysis of the shape of the inflection section of the profile information, and the first control point generated by the pore. The second control point generated by the inflection point may be rearranged on at least one of a panoramic image and a 3D volume rendered image to complete a neural tube.

A medical image processing apparatus according to another embodiment uses distance information between a data acquisition unit that acquires panoramic images and CT cross-sectional images, and a hard tissue inside a CT cross-sectional image constituting a curve line of the panoramic image. A control unit that searches for a small hole location and searches for an initial position of a neural tube using the profile information of a CT cross section, and then generates a neural tube line using the searched small hole location and initial position, and an output unit that displays the generated neural tube line on the screen. Include.

The data acquisition unit acquires a first CT cross-sectional image corresponding to the center position of the curve line of the panoramic image, and the control unit uses the distance difference between the edge position of the acquired first CT cross-sectional image and the start point of the hard tissue. It is possible to search and generate a first control point from the searched pore.

The control unit searches for the lowermost position of the jawbone at the bottom of the acquired first CT cross-sectional image, and moves the pixels horizontally from the edge position of the first CT cross-sectional image to the start of the jawbone while moving upward based on the lowermost position of the jawbone. Calculate the distance, and if the distance difference between the previous line distance and the current line distance is more than a preset distance according to the distance calculation result, the corresponding point is determined as a small hole, and a first control point for generating the end point of the neural tube line from the small hole is generated. I can.

The data acquisition unit acquires a second CT cross-sectional image corresponding to the edge position of the curve line of the panoramic image, and the control unit acquires a line profile for line data formed by moving pixels horizontally in the acquired second CT cross-sectional image. A second control point may be generated from the searched neural tube center point by generating a graph and searching for the center point of the neural tube through analysis of the change shape of the inflection point of the generated line profile graph.

The control unit searches for the lowermost position of the jawbone at the bottom of the acquired second CT cross-sectional image, sets the searched lowermost position of the jawbone as a starting point, and creates horizontal lines of a preset length by going up sequentially based on the starting point. A line profile graph indicating intensity values for each pixel for each horizontal line pixel data can be constructed.

The control unit searches for an inflection point corresponding to the jawbone and neural tube epidermis data among the inflection points formed in the line profile graph based on the intensity value, and searches the inflection point corresponding to the center point of the neural tube passing through the retrieved jawbone and neural tube epidermis data. A second control point can be created from

The control unit may reconstruct the line profile graph using B-Spline interpolation to analyze the shape of the inflection point change.

If the center point of the neural tube is not found through the analysis of the inflection point change shape, the control unit searches the lowermost position of the jawbone at the bottom of the second CT cross-sectional image, moves the pixel upward by a preset jawbone thickness from the searched lowermost position of the jawbone, and moves the pixel upward by the predetermined jawbone thickness. A temporary control point can be created from the point by moving the pixel upward by a predetermined neural tube position from the moved point.

The control unit terminates the search for the inflection point if the distance between the inflection point and the initially searched small hole through the analysis of the shape of the inflection section of the profile information is within a preset distance, and the first control point generated by the small hole and the second control generated by the inflection point The neural tube may be completed by rearranging the points on at least one of the panoramic image and the 3D volume rendering image.

According to the method for automatically generating a neural tube line and a medical image processing apparatus therefor according to an embodiment, since a neural tube is automatically searched for in an image, it is possible to save a user's time and increase the convenience of use. At this time, as the seed information such as initial information and foremen information necessary for automatic neural tube line generation is automatically searched through the software, the user does not need to separately input the corresponding seed information. The location of the neural tube can be identified immediately upon creation, which is a great help in the scalability of software.

Accurate neural tube location search is possible through distance information, profile information, and cross section search of neural tube in CT section image. The user can establish an implant placement plan in consideration of not damaging the automatically generated neural tube and improve efficiency by performing an implant surgery.

1 is a diagram showing the configuration of a medical image processing apparatus according to an embodiment of the present invention;
2 is a diagram showing the flow of a method for automatically generating a neural tube line according to an embodiment of the present invention;
3 is a view showing an example of searching for a foremen from an original image and a hard tissue image extracted from the original image according to an embodiment of the present invention;
4 is a diagram showing a CT cross-sectional image for setting a line profile graph generation section for retrieving an initial point of a neural tube according to an embodiment of the present invention;
5 is a view showing a line profile graph generated in the line profile graph generation section of FIG. 4;
6 is a CT cross-sectional image for explaining a method of searching a neural tube when a neural tube is not searched through an inflection point analysis according to an embodiment of the present invention;
7 is a diagram showing a panoramic image in which a neural tube is automatically generated by a method according to an embodiment of the present invention;
8 is a view showing a CT cross-sectional image in which a neural tube is automatically generated by a method according to an embodiment of the present invention;
9 is a view showing a 3D volume rendering image in which a neural tube that has escaped a small hole is automatically generated by a method according to an embodiment of the present invention;
10 is a diagram illustrating 2D and 3D images in which a neural tube is automatically generated by a method according to an embodiment of the present invention.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, only the present embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the art It is provided to completely inform the scope of the invention to those who have it, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same components throughout the specification.

In describing embodiments of the present invention, if it is determined that a detailed description of a known function or configuration may unnecessarily obscure the subject matter of the present invention, a detailed description thereof will be omitted, and terms to be described later are in the embodiments of the present invention. These terms are defined in consideration of the function of the user and may vary according to the intention or custom of users or operators. Therefore, the definition should be made based on the contents throughout this specification.

Combinations of each block in the attached block diagram and each step in the flowchart may be executed by computer program instructions (execution engines), and these computer program instructions are provided on a processor of a general purpose computer, special purpose computer or other programmable data processing device. As it may be mounted, its instructions executed by the processor of a computer or other programmable data processing device generate means for performing the functions described in each block of the block diagram or each step of the flowchart.

These computer program instructions may also be stored in a computer usable or computer readable memory that can be directed to a computer or other programmable data processing device to implement a function in a particular way, so that the computer usable or computer readable memory It is also possible to produce an article of manufacture containing instruction means for performing the functions described in each block of the block diagram or each step of the flow chart.

In addition, since computer program instructions can be mounted on a computer or other programmable data processing device, a series of operation steps are performed on a computer or other programmable data processing device to create a computer-executable process. It is also possible for the instructions to perform the data processing apparatus to provide steps for executing the functions described in each block of the block diagram and each step of the flowchart.

In addition, each block or each step may represent a module, segment, or part of code containing one or more executable instructions for executing specified logical functions, and in some alternative embodiments mentioned in the blocks or steps. It should be noted that it is also possible for functions to occur out of order. For example, two blocks or steps shown in succession may in fact be performed substantially simultaneously, and the blocks or steps may be performed in the reverse order of a corresponding function as necessary.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the embodiments of the present invention exemplified below may be modified in various other forms, and the scope of the present invention is not limited to the embodiments described below. Embodiments of the present invention are provided to more completely describe the present invention to those of ordinary skill in the art.

1 is a diagram showing a configuration of a medical image processing apparatus according to an embodiment of the present invention.

The medical image processing apparatus 1 is an electronic device capable of executing a medical image processing program such as a guide design program for dental implant surgery. Electronic devices include computers, notebook computers, laptop computers, tablet PCs, smartphones, mobile phones, personal media players (PMPs), personal digital assistants (PDAs), and the like. Medical image processing programs include scan programs, CAD programs, etc. in addition to guide design programs. In addition, it can be applied to programs for general medical image processing other than for dental implant surgery.

The image processing process using the program includes registration of a surgical patient, acquisition of CT data and oral model data of the registered patient, registration of CT data and oral model data, generation of curve lines from the matched image data, and wave using curve lines. It consists of a process including creating a panoramic image, determining the position and size of the crown model from the patient's oral model data, determining the position of the implant structure including the fixture from the patient's CT data, designing the guide shape, and outputting the final guide.

The present invention is configured to automatically generate and provide a neural tube from a CT cross-sectional image generated using CT data during the above process. At this time, the user checks the neural tube in the 2D image and the 3D image and establishes an operation plan in consideration of this.

Referring to FIG. 1, a medical image processing apparatus 1 according to an embodiment includes a data acquisition unit 10, a storage unit 12, a control unit 14, an input unit 16, and an output unit 18. .

The data acquisition unit 10 acquires image data from a patient. Image data necessary for generating a neural tube line may include CT data, oral model data, CT data, or a panoramic image obtained from oral model data. The data acquisition unit 10 may execute CT data and oral model data in a program or load data stored in a web page and a server.

The oral model data is data with information on actual teeth including damaged teeth. The oral model data may be obtained by scanning a plaster model created after a patient's mouth with a 3D scanner. As another example, it may be obtained by scanning the inside of the patient's oral cavity using a 3D intra-oral scanner. The oral model data may be in STL format. The obtained oral model data may be stored in the storage unit 12.

CT data can be obtained by generating a patient's head tomography images using computed tomography (CT), segmenting the boundary of the tooth from each tomography image, and then combining them into one. These oral model data and CT data are images obtained by photographing the maxillary teeth under the maxillary teeth with the patient's mouth open, the image obtained by photographing the mandibular teeth above the mandibular teeth with the mouth open, and the local area with the mouth closed. Includes images obtained, oral radiographs, etc. CT data may be in DICOM format. The acquired CT data may be stored in the storage unit 12.

The storage unit 12 stores various types of data, such as information necessary for the operation of the medical image processing apparatus 1 and information generated according to the operation. In the storage unit 12 according to an embodiment, oral model data and CT data of an individual patient are stored, and user request for oral model data and CT data of a specific patient among all oral model data and CT data during dental treatment simulation. It can be provided to the control unit 14 according to. At this time, the storage unit 12 stores images of the upper and lower teeth of an individual patient, and images of the upper and lower teeth that match the oral model data and CT data of a specific patient are stored at the user request. Accordingly, it can be provided to the control unit 14.

The control unit 14 controls each component while establishing an implant placement plan through control by a computer program. The control unit 14 manages screen information displayed on the screen through the output unit 18 and performs a simulation of placing a virtual fixture object in the dental image. The dental image in which the virtual fixture object is placed refers to a multidimensional image such as 2D or 3D that shows the patient's tooth arrangement created to establish an implant procedure plan. Various types of images, such as X-ray, CT, MRI, panoramic images, oral scan images, images generated through reconstruction, and images that match multiple images, can be used in the implant procedure plan.

The controller 14 according to an embodiment generates a virtual neural tube line from image data acquired through the data acquisition unit 10. The neural tube can be checked in a 3D volume-rendered image, but a 2D image is generally required to generate and confirm an accurate neural tube. For example, a panoramic image is generated from patient CT data or oral model data. As another example, a CT cross section image is generated from CT data. Panoramic images or CT cross-sectional images become images for neural tube line generation.

The controller 14 may extract a hard tissue area from the acquired image data and image-process the extracted hard tissue area to enhance each area. The hard tissue may be a tooth part, a jawbone, a cervical vertebra, and the like, and the controller 14 may search for a neural tube using distance information from the hard tissue in a CT cross-sectional image. The hard tissue separation may be performed using the intensity of the pixels constituting the image, and the intensity may be a density value such as a Hounsfield Unit (HU, hereinafter referred to as'HU') value. For example, a region with a HU value of 500 or more is extracted as a hard tissue region.

The control unit 14 automatically generates a neural tube line using the acquired image data. At this time, the control unit 14 searches for a foremen location using distance information from the hard tissue inside the CT cross-sectional image constituting the curve line of the panoramic image, and uses the profile information of the CT cross-sectional image to Search for the initial location. Then, a neural tube line is automatically generated by using the searched pore position and initial position.

According to the above-described automatic neural tube generation method, it is possible to eliminate the user hassle that occurs when the neural tube is manually generated. In the case of automatic neural tube search, a user input for seed information including small pore information and initial information is essential.However, since the control unit 14 according to an embodiment automatically searches for small pore information and initial information, small pore information and initial information There is no need for the user to enter.

Search for a small hole location For example, the data acquisition unit 10 acquires a first CT cross-sectional image corresponding to a center position of a curve line of a panoramic image. The control unit 14 searches for a small hole using the distance difference between the edge position of the first CT cross-sectional image acquired through the data acquisition unit 10 and the start point of the hard tissue, and generates a first control point from the searched small hole. A neural tube can be created by reconstructing the generated control points into a panoramic image and a 3D volume rendering image.

For example, the control unit 14 searches for the lowermost position of the jawbone at the lower end in the first CT cross-sectional image. The line distance is calculated by moving pixels horizontally from the edge position of the first CT cross-sectional image to the start point of the jawbone while moving upward based on the lowermost position of the jawbone. In this case, if the distance difference between the previous line distance and the current line distance is greater than or equal to a preset distance according to the distance calculation result, the corresponding point is determined as a small hole, and a first control point for generating an end point of the neural tube line is generated from the small hole. An example of small hole search will be described later with reference to FIG. 3.

Retrieving the initial position of the neural tube For example, the data acquisition unit 10 acquires a second CT cross-sectional image corresponding to the edge position of the curve line of the panoramic image. The control unit 14 generates a line profile graph for line data formed while moving pixels horizontally in the acquired second CT cross-sectional image, and searches for the center point of the neural tube through analysis of the change shape of the inflection point of the generated line profile graph. A second control point is created from the retrieved neural tube center point.

Generating a line profile graph For example, the control unit 14 searches for the lowermost position of the jawbone at the bottom of the second CT cross-sectional image, and sets the searched lowermost position of the jawbone as a starting point. Then, horizontal lines having a preset length are generated as they rise upward from the starting point in order, and then a line profile graph indicating the intensity value of each pixel for each generated horizontal line pixel data is constructed.

The control unit 14 according to an embodiment searches for an inflection point corresponding to the jawbone and neural tube epidermis data based on an intensity value among inflection points formed in the line profile graph. Then, a second control point is generated from the searched inflection point by searching for an inflection point corresponding to the center point of the neural tube passing through the retrieved jawbone and neural tube epidermal data. In this case, the control unit 14 may reconstruct the line profile graph by using B-Spline interpolation to analyze the shape of the change of the inflection point. An example of configuring a line profile graph and searching for an inflection point using the same will be described later with reference to FIGS. 4 and 5.

The control unit 14 according to an embodiment generates a temporary control point if the center point of the neural tube is not found through analysis of the shape of the change in the inflection point. For example, the lowermost position of the jawbone at the bottom of the second CT cross-sectional image is searched, and the pixel is moved upward by a predetermined jawbone thickness from the searched lowermost position of the jawbone. Thereafter, a pixel is moved upward by a predetermined neural tube position from a point moved upward by a predetermined jawbone thickness to generate a temporary control point from the corresponding point. An example of generating a temporary control point will be described later with reference to FIG. 6.

The controller 14 may terminate the search for the inflection point and generate a neural tube line if the distance between the inflection point and the initially retrieved small hole through the analysis of the shape of the inflection section of the profile information is within a preset distance. At this time, the control unit 14 completes the neural tube by rearranging the first control point generated by the small hole and the second control point generated by the inflection point on at least one of a panoramic image and a 3D volume rendering image.

The input unit 16 receives a user manipulation signal. The output unit 18 displays a screen. The output unit 18 displays image data (panoramic image, CT cross-sectional image, 3D image, etc.) generated through the control unit 14 on the screen. The CT cross-sectional image may be expressed as an axial view, a sagittal view, or a coronal view. In addition, the output unit 18 displays the neural tube line generation result on an image in the screen.

2 is a diagram illustrating a flow of a method for automatically generating a neural tube line according to an embodiment of the present invention.

1 and 2, the medical image processing apparatus 1 acquires a panoramic image for automatically generating a neural tube line (S210). For example, a panoramic image generated from patient CT data or oral model data is imported.

Subsequently, the medical image processing apparatus 1 acquires a first CT cross-section image corresponding to the center position of the curve line of the panoramic image among CT cross-section images in the transverse direction (S220). A panoramic image is generated using a curve line generated on CT data or oral model data, and the medical image processing apparatus 1 obtains a first CT cross-sectional image corresponding to the center position of the curve line.

Subsequently, the medical image processing apparatus 1 searches for a small hole while moving to the left or right based on the first CT cross-sectional image corresponding to the center position of the curve line of the panoramic image (S230), and the searched small hole is first A control point is generated (S240). At this time, the searched small pore may be set as an end point of the neural tube line. Small hole search method For example, a small hole is searched by using the difference in distance between the edge position (eg, the left end or the right end) of the first CT cross-sectional image and the starting point of the hard tissue. An example of small hole search will be described later with reference to FIG. 3. If a small hole is not found in the first CT cross-sectional image corresponding to the center position of the curve line, the CT cross-sectional image is moved to the left or right (S225) to search for small holes in the moved CT cross-sectional image (S230). CT cross-sectional image movement step (S225) and small hole search step (S230) are repeatedly performed until a small hole is searched.

Subsequently, the medical image processing apparatus 1 acquires a second CT cross-sectional image corresponding to a position of the left or right edge of the curve line of the panoramic image (S250). In addition, by analyzing the shape of the inflection section of the line profile graph of the obtained second CT cross-sectional image, the initial point of the neural tube is searched (S260), and a second control point is generated from the found initial point (S270). An example of searching for an initial point of a neural tube through analysis of the shape of an inflection section of a line profile will be described later with reference to FIGS. 4 and 5.

When all searches for the curve lines of the panoramic image are completed (S280), the medical image processing apparatus 1 generates a neural tube line (S290). For example, when the distance between the searched inflection point and the initially searched ostium becomes close by a preset distance through the analysis of the shape of the inflection section of the line profile graph, the search for the inflection point is terminated and a neural tube is created. At this time, the first control point generated by the small hole and the second control point generated by the inflection point are rearranged on at least one of a panoramic image and a 3D volume rendered image to complete a neural tube.

3 is a diagram illustrating an example of searching for a small hole in an original image and a hard tissue image extracted from the original image according to an embodiment of the present invention.

1 and 3, the medical image processing apparatus 1 extracts a hard tissue image 32 from an original image 31. For example, a first CT cross-sectional image corresponding to the center position of the curve line of the panoramic image is acquired from the captured CT data, and the obtained first CT cross-sectional image is used as a target to determine the soft tissue area. The hard tissue image 32 is obtained by removing and extracting the hard tissue area. The hard tissue contains the jawbone.

Subsequently, the lowermost position of the lower jawbone 320-1 is searched in the extracted hard tissue image 32. Then, pixels from the edge of the image, that is, the left or right end position (left end position in FIG. 3) to the start position of the jawbones 320-1 and 320-2, are sequentially moved up based on the lowermost position of the jawbone. The distances of the constituting horizontal lines 330 are calculated. According to the distance calculation result, if the distance difference 340 between the previous line distance 330-1 and the current line distance 330-2 is equal to or greater than a preset distance (eg, 3 mm), the corresponding point 350 is small. By judging by, the search for small pores is terminated and a control point is created for the corresponding small hole. Here, the vertical axis coordinate of the corresponding point 350 is estimated as a position corresponding to the height of the current line distance 330-2, and the horizontal axis coordinate is the jawbone 320-1 meeting the previous line distance 330-1 It can be estimated between the starting point and the starting point of the jawbone 320-2 meeting the current line distance 330-2.

4 is a diagram showing a CT cross-sectional image for setting a line profile graph generation section for retrieving an initial point of a neural tube according to an embodiment of the present invention, and FIG. 5 is a line profile generated in the line profile graph generation section of FIG. 4 It is a diagram showing a graph.

1, 4, and 5, the medical image processing apparatus 1 targets the second CT cross-sectional image 40 corresponding to the position of the left or right edge of the curve line of the panoramic image. Search for the lowest location. The line profile for the horizontal lines 420 constituting pixels having a preset distance while setting the searched lowermost position of the jawbone as the start point 400, and rising in sequence 410 based on the start point 400 Each graph is constructed. In this case, the preset distance may be, for example, ±15mm: 30mm based on the starting point 400.

The medical image processing apparatus 1 generates a line profile graph 50 as shown in FIG. 5 for each of the horizontal lines 420. The horizontal axis of the predetermined line profile graph 50 refers to a pixel having a preset distance (for example, 30 mm) left and right from the starting point, and the vertical axis refers to an intensity value for each pixel. The intensity value can be determined from the HU value. The line profile graph 50 of FIG. 5 is generated as many as the number of horizontal lines in FIG. 4, and an example of a line profile graph for one horizontal line is as shown in FIG. 5.

The medical image processing apparatus 1 according to an exemplary embodiment searches for an initial point of a neural tube through analysis of a change in the shape of an inflection point in a line profile graph. At this time, the initial point of the neural tube includes the point of movement of the neural tube.

Referring to FIG. 5, an example of searching for a neural tube through an analysis of a change in the shape of an inflection point is searched for jawbone and neural tube epidermis data based on intensity values for inflection points formed in the line profile graph 50. Then, a second control point is generated from the searched inflection point by searching for an inflection point corresponding to the center point of the neural tube passing through the retrieved jawbone and neural tube epidermal data. For example, the position of the inflection points (510-1, 510-2) having the largest intensity value is estimated as the jaw, and the position of the inflection points (520-1, 520-2) having the next intensity is estimated as neural tube epidermal data. In addition, the inflection point 530 formed between the positions of the inflection points 520-1 and 520-2 estimated as neural tube epidermal data may be estimated as the position of the neural tube center point.

At this time, for ease of calculating the inflection point, the inflection point may be calculated by reducing the sensitivity of noise and unnecessary data by using B-spline interpolation. The B-spline curve is a smoother curve than the Hermite or Bezier curve. When displaying a curve in 3D, the first and second derivatives of both ends are continuous. It is a curve defined as possible.

If the neural tube is not searched despite analyzing the shape of the inflection point of the line profile graph for all horizontal line data for a predetermined CT section image, i.e., when the inflection point analysis fails, it moves to the next CT section image and repeats the neural tube search. Can be done. This is the case in patients whose neural tube cross section is difficult to see.

6 is a CT cross-sectional image for explaining a method of searching a neural tube when a neural tube is not searched through an inflection point analysis according to an embodiment of the present invention.

In the case where the neural tube is not searched even though the inflection point analysis of FIG. 5 is performed, that is, the neural tube section is not well analyzed, the program may present an arbitrary guide point to the user. For example, the error of automatic neural tube generation is minimized by moving a preset distance based on the lowermost position of the jawbone of the CT cross-sectional image 60 to generate a guide point for retrieving a neural tube position. This process is only used when searching for inflection points is not successful. For example, the position of the lowermost jawbone of the CT cross-sectional image 60 is searched. Then, it moves upward by a preset thickness of the jawbone (eg, 4.5mm) 610 from the lowermost end of the searched jawbone. Then, the position 630 is further moved upward by a preset neural tube distance (eg, 5.5 mm) 620 to determine the corresponding position 630 as a temporary neural tube control point. The temporary neural tube control point becomes the guide point for neural tube line creation.

7 is a diagram illustrating a panoramic image in which a neural tube is automatically generated by a method according to an embodiment of the present invention.

Referring to FIG. 7, control points generated through each CT cross-sectional image may be reconstructed into a panoramic image 70 to generate a neural tube. In FIG. 7, a virtual neural tube 700 is generated on the left side of the panoramic image 70. The virtual neural tube may be generated only in one area of the left or the right side with respect to the center of the panoramic image, and may be generated as multiple neural tubes at the same time as the left and right side. The left and right neural tubes are divided into left and right neural tubes based on the lower curve line corresponding to the anterior tooth number.

8 is a diagram illustrating a CT cross-sectional image in which a neural tube is automatically generated by a method according to an embodiment of the present invention.

Referring to FIG. 8, a neural tube 800 is automatically generated by searching for a small pore or an initial point in a CT cross-sectional image 80 of a curve line of a panoramic image.

9 is a diagram illustrating a 3D volume rendering image in which a neural tube that has escaped a small hole is automatically generated by a method according to an embodiment of the present invention.

Referring to FIG. 9, when the control point generated from each CT cross-sectional image is reconstructed in the 3D volume rendering image 90, a neural tube 900 that has escaped the small hole may be generated.

10 is a diagram illustrating 2D and 3D images in which a neural tube is automatically generated by a method according to an embodiment of the present invention.

10, a neural tube 1000 in a coronal view image 100, a neural tube 1020 in a sagittal view image 102, an axial view The neural tube 1040 in the image 104 and the neural tube 1060 in the 3D view image 106 may be identified, respectively. The coronal view image 100, the sagittal view image 102, and the axial view image 104 are CT cross-sectional images. Control points generated from each CT cross-sectional image may be reconstructed into the 3D view image 106 to automatically generate a neural tube line.

So far, the present invention has been looked at around the embodiments. Those of ordinary skill in the art to which the present invention pertains will be able to understand that the present invention may be implemented in a modified form without departing from the essential characteristics of the present invention. Therefore, the disclosed embodiments should be considered from an illustrative point of view rather than a limiting point of view. The scope of the present invention is shown in the claims rather than the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

Claims (20)

Obtaining panoramic images and CT cross-sectional images;
Retrieving a small hole location using distance information between a hard tissue within a CT cross-sectional image constituting a curved line of the panoramic image;
Searching for an initial position of a neural tube using profile information of the CT cross-sectional image; And
Generating a neural tube line by using the searched pore position and initial position;
Automatic neural tube generation method comprising a. The method of claim 1, wherein the step of searching for a small hole location
Acquiring a first CT cross-sectional image corresponding to the center position of the curve line of the panoramic image; And
Searching for a small hole using the difference in distance between the edge position of the obtained first CT cross-sectional image and the starting point of the hard tissue, and generating a first control point from the searched small hole;
Automatic neural tube generation method comprising a. The method of claim 2, wherein generating the first control point
Searching for a lowermost position of the jawbone at the lower end of the obtained first CT cross-sectional image;
Calculating a line distance by moving pixels horizontally from the edge position of the first CT cross-sectional image to the start point of the jawbone while rising upward based on the lowermost position of the jawbone;
Determining a corresponding point as a small hole if the distance difference between the previous line distance and the current line distance is greater than or equal to a preset distance according to the distance calculation result; And
Generating a first control point for generating an end point of a neural tube line from the pore;
Automatic neural tube generation method comprising a. The method of claim 1, wherein the automatic neural tube generation method
Searching for a small hole in the moved CT cross-sectional image while moving the CT cross-sectional image when the small hole search fails;
Automatic neural tube generation method, characterized in that it further comprises. The method of claim 1, wherein the step of searching for the initial position of the neural tube
Obtaining a second CT cross-sectional image corresponding to an edge position of a curve line of the panoramic image;
Generating a line profile graph for line data formed while moving pixels horizontally in the obtained second CT cross-sectional image; And
Searching for a center point of a neural tube through analysis of a change shape of an inflection point of the generated line profile graph and generating a second control point from the retrieved center point of the neural tube;
Automatic neural tube generation method comprising a. The method of claim 5, wherein generating the line profile graph comprises:
Searching for a lowermost position of the jawbone below the acquired second CT cross-sectional image;
Setting the searched lowermost position of the jawbone as a starting point, and generating horizontal lines of a preset length while sequentially rising upward based on the starting point; And
Constructing a line profile graph indicating an intensity value for each pixel for each of the generated horizontal line pixel data;
Automatic neural tube generation method comprising a. The method of claim 5, wherein generating the second control point comprises:
Searching for an inflection point corresponding to the jawbone and neural tube epidermis data based on an intensity value among the inflection points formed in the line profile graph; And
Generating a second control point from the retrieved inflection point by searching for an inflection point corresponding to a center point of a neural tube passing through the retrieved jawbone and neural tube epidermal data;
Automatic neural tube generation method comprising a. The method of claim 5, wherein the step of searching for the initial position of the neural tube
Reconstructing a line profile graph using B-Spline interpolation to analyze the shape of an inflection point change;
Automatic neural tube generation method, characterized in that it further comprises. The method of claim 5, wherein the step of searching for the initial position of the neural tube
If the center point of the neural tube is not found through the analysis of the shape of the inflection point, moving to the next CT cross-sectional image to the left or right to generate a line profile graph, and analyzing the shape of the inflection point in the generated line profile graph to search for the center point of the neural tube;
Automatic neural tube generation method, characterized in that it further comprises. The method of claim 5, wherein the step of searching for the initial position of the neural tube
Retrieving the lowermost position of the jawbone at the bottom of the second CT cross-sectional image if the center point of the neural tube is not found through analysis of the shape of the inflection point change;
Moving a pixel upward by a predetermined jawbone thickness from the searched jawbone lowermost position; And
Generating a temporary control point from the point by moving a pixel upward by a predetermined neural tube position from a point moved upward by a preset jawbone thickness;
Automatic neural tube generation method, characterized in that it further comprises. The method of claim 1, wherein generating the neural tube line
Terminating the search for the inflection point if the distance between the inflection point and the initially retrieved small hole is within a preset distance through analysis of the shape of the inflection section of the profile information; And
Relocating the first control point generated by the small hole and the second control point generated by the inflection point on at least one of a panoramic image and a 3D volume rendering image to complete a neural tube;
Automatic neural tube generation method comprising a. A data acquisition unit that acquires panoramic images and CT cross-sectional images;
The location of the pore is searched using the distance information from the hard tissue inside the CT section image that constitutes the curved line of the panoramic image, and the initial location of the neural tube is searched using the profile information of the CT section image. A control unit that generates a neural tube line using the position; And
An output unit that displays the generated neural tube line on the screen;
Medical image processing apparatus comprising a. The method of claim 12, wherein the data acquisition unit
Acquire a first CT cross-sectional image corresponding to the center position of the curve line of the panoramic image,
The control unit
A medical image processing apparatus, comprising: searching for a small hole using a distance difference between an edge position of the obtained first CT cross-sectional image and a starting point of a hard tissue, and generating a first control point from the searched small hole. The method of claim 13, wherein the control unit
Search the lowermost position of the lower jawbone in the acquired first CT cross-sectional image,
The line distance is calculated by moving pixels horizontally from the edge of the first CT cross-sectional image to the start of the jawbone while moving upward from the lowermost position of the jawbone,
According to the distance calculation result, if the distance difference between the previous line distance and the current line distance is greater than or equal to a preset distance, the corresponding point is determined as a small hole,
A medical image processing apparatus comprising generating a first control point for generating an end point of a neural tube line from a small hole. The method of claim 12, wherein the data acquisition unit
Acquire a second CT cross-sectional image corresponding to the edge position of the curve line of the panoramic image,
The control unit
In the obtained second CT cross-sectional image, a line profile graph for line data formed by moving a pixel horizontally is generated, and the center point of the neural tube is searched through analysis of the change shape of the inflection point of the generated line profile graph. A medical image processing apparatus, characterized in that generating a second control point. The method of claim 15, wherein the control unit
Search the lowermost position of the jawbone at the bottom of the acquired second CT cross-sectional image,
The searched lowermost position of the jawbone is set as the starting point, and horizontal lines of a preset length are created by going up sequentially from the starting point,
A medical image processing apparatus comprising constructing a line profile graph indicating an intensity value for each pixel for each generated horizontal line pixel data. The method of claim 15, wherein the control unit
Among the inflection points formed in the line profile graph, the inflection point corresponding to the jawbone and neural tube epidermis data is searched based on the intensity value, and the inflection point corresponding to the center point of the neural tube passing through the searched jawbone and neural tube epidermis data is searched and removed from the searched inflection point. 2 A medical image processing apparatus, characterized in that generating control points. The method of claim 15, wherein the control unit
A medical image processing apparatus comprising reconstructing a line profile graph using B-Spline interpolation to analyze the shape of an inflection point change. The method of claim 15, wherein the control unit
If the center point of the neural tube is not found through the analysis of the inflection point change shape, the lowermost position of the jawbone at the bottom of the second CT cross-sectional image is searched.
Moves the pixel from the searched lowermost position of the jawbone up to the preset jawbone thickness,
A medical image processing apparatus, characterized in that for generating a temporary control point from the point by moving a pixel upward by a predetermined neural tube position from a point moved upward by a predetermined jawbone thickness. The method of claim 11, wherein the control unit
If the distance between the inflection point retrieved through the shape analysis of the inflection section of the profile information and the initially retrieved small hole is within a preset distance, the inflection point search is terminated,
A medical image processing apparatus, comprising rearranging a first control point generated by a small hole and a second control point generated by an inflection point on at least one of a panoramic image and a 3D volume rendering image to complete a neural tube.

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