Head symmetry axis identification method and device, storage medium and computer equipment

文档序号：1954776 发布日期：2021-12-10 浏览：10次中文

阅读说明：本技术 头部对称轴识别方法及装置、存储介质、计算机设备 (Head symmetry axis identification method and device, storage medium and computer equipment ) 是由付璐才品嘉李戈于 2021-08-05 设计创作，主要内容包括：本申请公开了一种头部对称轴识别方法及装置、存储介质、计算机设备,该方法包括：识别目标头部医学图像中脑组织区域对应的脑组织边缘链码以及链码切割点,依据脑组织边缘链码与链码切割点的位置关系,提取脑组织边缘链码中的前段上矢状窦链码和后段上矢状窦链码；根据前段上矢状窦链码中各点的前段曲率值确定前段候选角点,根据后段上矢状窦链码中各点的后段曲率值确定后段候选角点；检测前段候选角点在前段上矢状窦链码中的第一位置特征,基于第一位置特征确定前段目标角点,检测后段候选角点在后段上矢状窦链码中的第二位置特征,基于第二位置特征确定后段目标角点；依据前段目标角点与后段目标角点的连线,确定脑组织区域对应的头部对称轴。(The application discloses a head symmetry axis identification method and device, a storage medium and computer equipment, wherein the method comprises the following steps: recognizing a brain tissue edge chain code and a chain code cutting point corresponding to a brain tissue region in the target head medical image, and extracting a front section upper sagittal sinus chain code and a rear section upper sagittal sinus chain code in the brain tissue edge chain code according to the position relation of the brain tissue edge chain code and the chain code cutting point; determining a front section candidate angular point according to front section curvature values of all points in the front section upper sagittal sinus chain code, and determining a rear section candidate angular point according to rear section curvature values of all points in the rear section upper sagittal sinus chain code; detecting a first position characteristic of the candidate angular point of the front section in the upper sagittal sinus chain code of the front section, determining a target angular point of the front section based on the first position characteristic, detecting a second position characteristic of the candidate angular point of the rear section in the upper sagittal sinus chain code of the rear section, and determining a target angular point of the rear section based on the second position characteristic; and determining a head symmetry axis corresponding to the brain tissue region according to a connecting line of the front section target corner point and the rear section target corner point.)

1. A head symmetry axis recognition method is characterized by comprising the following steps:

recognizing a brain tissue edge chain code and a chain code cutting point corresponding to a brain tissue region in a target head medical image, and extracting a front section superior sagittal sinus chain code and a rear section superior sagittal sinus chain code in the brain tissue edge chain code according to the position relation between the brain tissue edge chain code and the chain code cutting point;

determining front section candidate angular points according to front section curvature values of all points in the front section upper sagittal sinus chain code, and determining rear section candidate angular points according to rear section curvature values of all points in the rear section upper sagittal sinus chain code;

detecting a first position feature of the front-segment candidate angular point in the front-segment upper sagittal sinus chain code, determining a front-segment target angular point based on the first position feature, detecting a second position feature of the rear-segment candidate angular point in the rear-segment upper sagittal sinus chain code, and determining a rear-segment target angular point based on the second position feature;

and determining a head symmetry axis corresponding to the brain tissue region according to a connecting line of the front section target corner point and the rear section target corner point.

2. The method according to claim 1, wherein the extracting of anterior superior sagittal sinus chain codes and posterior superior sagittal sinus chain codes in the brain tissue edge chain codes according to the position relationship between the brain tissue edge chain codes and the chain code cutting points specifically comprises:

determining portions of the brain tissue edge chain code above and below a horizontal line corresponding to the chain code cut point as the anterior superior sagittal sinus chain code and the posterior superior sagittal sinus chain code, respectively, wherein the chain code cut point includes a centroid of the brain tissue region;

and respectively intercepting the middle parts of the front section upper sagittal sinus chain code and the rear section upper sagittal sinus chain code according to a preset proportion.

3. The method according to claim 1, wherein determining an anterior segment candidate corner point according to anterior segment curvature values of each point in the anterior segment higher sagittal sinus chain code, and determining a posterior segment candidate corner point according to posterior segment curvature values of each point in the posterior segment higher sagittal sinus chain code, specifically comprises:

acquiring a plurality of preset front section chord length parameters and a plurality of preset rear section chord length parameters;

respectively calculating a plurality of anterior segment curvature values of each point in the anterior segment superior sagittal sinus chain code according to the preset anterior segment chord length parameters, and determining the anterior segment candidate angular point based on curvature products corresponding to the plurality of anterior segment curvature values;

and respectively calculating a plurality of rear section curvature values of each point in the rear section upper sagittal sinus chain code according to the preset rear section chord length parameters, and determining the rear section candidate angular point based on curvature products corresponding to the rear section curvature values.

4. The method according to claim 3, wherein said detecting a first location feature of the candidate corner points of the previous segment in the previous segment top sagittal sinus chain code and determining a previous segment target corner point based on the first location feature, and detecting a second location feature of the candidate corner points of the next segment in the next segment top sagittal sinus chain code and determining a next segment target corner point based on the second location feature, specifically comprises:

respectively determining a preset front section distance corresponding to each preset front section chord length parameter and a preset rear section distance corresponding to each preset rear section chord length parameter;

selecting a plurality of groups of first reference points which are away from two sides of any front-section candidate angular point by the preset front-section distance from the front-section upper sagittal sinus chain code, wherein each group of first reference points comprises two reference points;

respectively establishing a first straight line and a second straight line which passes through any front-segment candidate angular point and is perpendicular to the first straight line based on multiple groups of first reference points, and determining that the first position characteristic of any front-segment candidate angular point is a salient point characteristic when the intersection point of the first straight line and the second straight line corresponding to at least one group of first reference points is in the brain tissue region;

after deleting the front-section candidate angular point with the salient point characteristic, screening out a front-section target angular point with the maximum curvature product;

selecting a plurality of groups of second reference points which are away from two sides of any rear-segment candidate angular point by the preset rear-segment distance from the rear-segment upper sagittal sinus chain code, wherein each group of second reference points comprises two reference points;

respectively establishing a third straight line and a fourth straight line which passes through any one rear-section candidate angular point and is perpendicular to the third straight line based on a plurality of groups of second reference points, and determining that the second position characteristic of any rear-section candidate angular point is a salient point characteristic when the intersection point of the third straight line and the fourth straight line corresponding to at least one group of second reference points is in the brain tissue area;

and after the candidate corner points of the rear section with the salient point characteristics are deleted, screening out the target corner points of the rear section with the maximum curvature product.

5. The method of any one of claims 1 to 4, wherein the target head medical image comprises an image of a plurality of slices; the determining a head symmetry axis corresponding to the brain tissue region according to a connection line between the front section target corner point and the rear section target corner point specifically includes:

determining a first connecting line between a first front section target corner point and a first rear section target corner point corresponding to the first target layer and a second connecting line between a second front section target corner point and a second rear section target corner point corresponding to the second candidate group;

establishing a symmetry axis identification surface according to the first connecting line and the second connecting line;

and acquiring the intersection line of the symmetry axis identification surface and the brain tissue region corresponding to each fault, and determining the intersection line as the head symmetry axis of each fault.

6. The method of claim 5, wherein the first target layer comprises a plurality of layers, and the second target layer comprises a plurality of layers; before the identifying a brain tissue edge chain code and a chain code cut point corresponding to a brain tissue region in the target head medical image, the method further includes:

acquiring the target head medical image, respectively carrying out contour recognition on the image of each fault, and determining brain tissue areas of a plurality of faults, wherein the target head medical image comprises a CT (computed tomography) image and/or an MR (magnetic resonance) image;

acquiring a first candidate fault with the largest brain tissue area in a plurality of faults, and taking at least two faults respectively adjacent to the front and the back of the first candidate fault and the first candidate fault as a first target layer;

acquiring a second candidate fault with the smallest difference between the area of a brain tissue region in the multiple faults and a preset area, and taking at least two faults respectively adjacent to the front part and the rear part of the second candidate fault and the second candidate fault as a second target layer;

correspondingly, the identifying of the brain tissue edge chain code and the chain code cutting point corresponding to the brain tissue region in the target head medical image specifically includes:

and identifying brain tissue edge chain codes and chain code cutting points of brain tissue areas corresponding to the first target layer and the second target layer.

7. The method of claim 6, wherein prior to constructing the symmetry-axis identification surface, the method further comprises:

filtering the front-section target angular points according to the relative positions of the front-section target angular points in the corresponding front-section upper sagittal sinus chain codes in the first target layer, and filtering the rear-section target angular points according to the relative positions of the rear-section target angular points in the corresponding rear-section upper sagittal sinus chain codes in the first target layer, so that the relative position difference of the front-section target angular points and the relative position difference of the rear-section target angular points in the filtered first target layer are smaller than a preset difference value;

filtering the front-section target angular points according to the relative positions of the front-section target angular points in the corresponding front-section upper sagittal sinus chain codes in the second target layer, and filtering the rear-section target angular points according to the relative positions of the rear-section target angular points in the corresponding rear-section upper sagittal sinus chain codes in the second target layer, so that the relative position difference of the front-section target angular points and the relative position difference of the rear-section target angular points in the second target layer after filtering are both smaller than a preset difference value;

determining a first front section target corner point of the first target layer according to the curvature value of each front section target corner point in the first target layer, and determining a first rear section target corner point of the first target layer according to the curvature value of each rear section target corner point in the first target layer;

and determining a second front section target corner point of the second target layer according to the curvature value of each front section target corner point in the second target layer, and determining a second rear section target corner point of the second target layer according to the curvature value of each rear section target corner point in the second target layer.

8. A head symmetry axis recognition apparatus, comprising:

the brain tissue identification module is used for identifying a brain tissue edge chain code and a chain code cutting point corresponding to a brain tissue region in a target head medical image, and extracting a front section upper sagittal sinus chain code and a rear section upper sagittal sinus chain code in the brain tissue edge chain code according to the position relation between the brain tissue edge chain code and the chain code cutting point;

a candidate angular point determining module, configured to determine a front-segment candidate angular point according to the front-segment curvature values of the points in the front-segment upper sagittal sinus chain code, and determine a rear-segment candidate angular point according to the rear-segment curvature values of the points in the rear-segment upper sagittal sinus chain code;

a target corner determination module, configured to detect a first position feature of the front candidate corner in the front upper sagittal sinus chain code, determine a front target corner based on the first position feature, detect a second position feature of the back candidate corner in the back upper sagittal sinus chain code, and determine a back target corner based on the second position feature;

and the symmetry axis identification module is used for determining a head symmetry axis corresponding to the brain tissue region according to a connecting line of the front section target corner point and the rear section target corner point.

9. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of any of claims 1 to 7.

10. A computer device comprising a storage medium, a processor and a computer program stored on the storage medium and executable on the processor, characterized in that the processor implements the method of any one of claims 1 to 7 when executing the computer program.

Technical Field

The present application relates to the field of medical imaging technologies, and in particular, to a method and an apparatus for identifying a head symmetry axis, a storage medium, and a computer device.

Background

Brain diseases greatly affect the quality of life of patients, so that diagnosis and treatment thereof are a hot problem in the global health field. The identification of the brain symmetry axis is an indispensable key step in many aspects such as diagnosis and treatment of brain diseases. For example, the method has important significance for the cerebral midline shift caused by cerebral hemorrhage and edema, brain tumor occupation and other diseases, the quantitative calculation of left and right cerebral ischemia and hemorrhage volume, the prediction of cerebral injury caused by head radiotherapy and the like.

The basis for determining the symmetry axis of the brain is mainly the brain morphology and tissue structure characteristics. The existing method for identifying the symmetry axis of the head mainly comprises the following steps: a three-dimensional least square method fitting method and a brain tissue structure symmetry identification method. The three-dimensional least square method fitting method depends on the complete and normal structure of the sickle and the high-resolution image quality, but the brain of a patient is often accompanied by diseases, and the imaging quality of different hospital equipment is difficult to guarantee. The brain tissue structure symmetry recognition method depends on the internal structure of normal brain tissue and the head position correction condition during scanning, and in the technology depending on symmetry, the symmetry of a recognition target (such as eyes) can be influenced by brain tissue lesion and the head position non-ideal during scanning. In summary, the accuracy of the head symmetry axis identification needs to be improved at present.

Disclosure of Invention

In view of this, the present application provides a method and an apparatus for identifying a head symmetry axis, a storage medium, and a computer device, which are helpful for improving accuracy of identifying the head symmetry axis.

According to an aspect of the present application, there is provided a head symmetry axis identification method, including:

and determining a head symmetry axis corresponding to the brain tissue region according to a connecting line of the front section target corner point and the rear section target corner point.

Optionally, the extracting an anterior superior sagittal sinus chain code and a posterior superior sagittal sinus chain code in the brain tissue edge chain code according to the position relationship between the brain tissue edge chain code and the chain code cutting point specifically includes:

and respectively intercepting the middle parts of the front section upper sagittal sinus chain code and the rear section upper sagittal sinus chain code according to a preset proportion.

Optionally, the determining a front-segment candidate corner point according to the front-segment curvature values of each point in the front-segment superior sagittal sinus chain code, and determining a back-segment candidate corner point according to the back-segment curvature values of each point in the back-segment superior sagittal sinus chain code specifically include:

acquiring a plurality of preset front section chord length parameters and a plurality of preset rear section chord length parameters;

Optionally, the detecting a first position feature of the front candidate corner in the front upper sagittal sinus chain code, and determining a front target corner based on the first position feature, and detecting a second position feature of the back candidate corner in the back upper sagittal sinus chain code, and determining a back target corner based on the second position feature specifically includes:

after deleting the front-section candidate angular point with the salient point characteristic, screening out a front-section target angular point with the maximum curvature product;

Optionally, the target head medical image comprises images of a plurality of slices; the determining a head symmetry axis corresponding to the brain tissue region according to a connection line between the front section target corner point and the rear section target corner point specifically includes:

establishing a symmetry axis identification surface according to the first connecting line and the second connecting line;

Optionally, the first target layer comprises a plurality of layers, and the second target layer comprises a plurality of layers; before the identifying a brain tissue edge chain code and a chain code cut point corresponding to a brain tissue region in the target head medical image, the method further includes:

correspondingly, the identifying of the brain tissue edge chain code and the chain code cutting point corresponding to the brain tissue region in the target head medical image specifically includes:

and identifying brain tissue edge chain codes and chain code cutting points of brain tissue areas corresponding to the first target layer and the second target layer.

Optionally, before the constructing the symmetry-axis identification plane, the method further includes:

filtering the front-section target angular points according to the relative positions of the front-section target angular points in the corresponding front-section upper sagittal sinus chain codes in the second target layer, and filtering the rear-section target angular points according to the relative positions of the rear-section target angular points in the corresponding rear-section upper sagittal sinus chain codes in the second target layer, so that the relative position difference of the front-section target angular points and the relative position difference of the rear-section target angular points in the second target layer after filtering are both smaller than a preset difference value;

According to another aspect of the present application, there is provided a head symmetry-axis recognition apparatus including:

Optionally, the brain tissue identification module is specifically configured to:

and respectively intercepting the middle parts of the front section upper sagittal sinus chain code and the rear section upper sagittal sinus chain code according to a preset proportion.

Optionally, the candidate corner point determining module is specifically configured to:

acquiring a plurality of preset front section chord length parameters and a plurality of preset rear section chord length parameters;

Optionally, the target corner point determining module is specifically configured to:

after deleting the front-section candidate angular point with the salient point characteristic, screening out a front-section target angular point with the maximum curvature product;

Optionally, the target head medical image comprises images of a plurality of slices; the symmetry axis identification module is specifically configured to:

establishing a symmetry axis identification surface according to the first connecting line and the second connecting line;

Optionally, the first target layer comprises a plurality of layers, and the second target layer comprises a plurality of layers; the device further comprises:

a target layer acquisition module, configured to acquire the target head medical image before identifying a brain tissue edge chain code and a chain code cutting point corresponding to a brain tissue region in the target head medical image, perform contour identification on an image of each slice, and determine brain tissue regions of multiple slices, where the target head medical image includes a CT image and/or an MR image; and the number of the first and second groups,

accordingly, the brain tissue identification module is specifically configured to:

and identifying brain tissue edge chain codes and chain code cutting points of brain tissue areas corresponding to the first target layer and the second target layer.

Optionally, the apparatus further comprises:

a target corner screening module, configured to filter the front-segment target corners according to relative positions of front-segment target corners in corresponding front-segment up-sagittal sinus chain codes in the first target layer before the symmetry axis identification plane is constructed, and filter the back-segment target corners according to relative positions of back-segment target corners in corresponding back-segment up-sagittal sinus chain codes in the first target layer, so that a relative position difference between the front-segment target corners and a relative position difference between the back-segment target corners in the first target layer after filtering are both smaller than a preset difference value; and the number of the first and second groups,

filtering the front-section target angular points according to the relative positions of the front-section target angular points in the corresponding front-section upper sagittal sinus chain codes in the second target layer, and filtering the rear-section target angular points according to the relative positions of the rear-section target angular points in the corresponding rear-section upper sagittal sinus chain codes in the second target layer, so that the relative position difference of the front-section target angular points and the relative position difference of the rear-section target angular points in the second target layer after filtering are both smaller than a preset difference value; and the number of the first and second groups,

According to yet another aspect of the present application, there is provided a storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described head symmetry axis recognition method.

According to yet another aspect of the present application, there is provided a computer device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, the processor implementing the above-mentioned head symmetry axis identification method when executing the program.

By means of the technical scheme, the head symmetry axis identification method and device, the storage medium and the computer equipment provided by the application identify the brain tissue edge chain codes and the chain code cutting points from the brain tissue area in the target head medical image, and the brain tissue edge chain codes are cut into anterior segment superior sagittal sinus chain codes and posterior segment superior sagittal sinus chain codes according to the chain code cutting points, and anterior segment candidate angular points and posterior segment candidate angular points are found out further according to curvature values of each point of the anterior segment superior sagittal sinus chain codes and the posterior segment superior sagittal sinus chain codes, and screening concave points in the candidate angular points based on concave-convex position characteristics of the front-segment candidate angular point in the front-segment sagittal sinus chain code and the rear-segment candidate angular point in the rear-segment sagittal sinus chain code to respectively serve as a front-segment target angular point and a rear-segment target angular point, and finally determining a head symmetry axis corresponding to the brain tissue region according to a connecting line of the front-segment target angular point and the rear-segment target angular point. According to the embodiment of the application, the connecting line of the anterior superior sagittal sinus and the posterior superior sagittal sinus is used as the characteristic of a head symmetry axis, the brain tissue edge chain codes of the brain tissue region outline which is not easily affected by brain tissue lesion and image quality are selected, the superior sagittal sinus characteristic embodied by the brain tissue edge chain codes is used for identifying the anterior segment target angular point and the posterior segment target angular point, the determination of the symmetry axis is realized, the problem of inaccurate identification of the symmetry axis caused by the influence of the brain tissue lesion or the image quality on the tissue definition is solved, the accuracy and the robustness of the head symmetry axis identification are improved, the angular point identification is carried out on one-dimensional data, namely the chain codes, the identification speed is high, and the efficiency is high.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a schematic flow chart illustrating a head symmetry axis identification method according to an embodiment of the present application;

fig. 2 is a schematic diagram illustrating an image of a brain tissue region provided by an embodiment of the present application;

fig. 3 is a schematic flow chart of another head symmetry axis identification method provided in the embodiment of the present application;

fig. 4 shows a schematic structural diagram of a head symmetry axis recognition apparatus provided in an embodiment of the present application.

Detailed Description

The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.

In the present embodiment, a head symmetry axis identification method is provided, as shown in fig. 1, the method includes:

step 101, identifying a brain tissue edge chain code and a chain code cutting point corresponding to a brain tissue region in a target head medical image, and extracting a front section superior sagittal sinus chain code and a rear section superior sagittal sinus chain code in the brain tissue edge chain code according to a position relation between the brain tissue edge chain code and the chain code cutting point;

the target head medical image in the embodiment of the application can be a head CT image or an MR image of a patient, can be a CT flat scan (NCCT), a CT perfusion imaging (CTP) and a CT blood vessel imaging (CTA), can also be an MR perfusion imaging (PWI), an MR diffusion imaging (DWI) and the like, and is poor in head symmetry axis identification accuracy caused by avoiding influence of brain tissue lesion or image quality on tissue definition of the patient.

In the above embodiment, first, after a target head medical image is obtained, the contour of a brain tissue region in the image is identified, a brain tissue edge chain code corresponding to the contour of the brain tissue region is obtained, and a chain code cutting point in the brain tissue region is identified, where the chain code cutting point may be specifically a centroid or a center of the brain tissue region, and the chain code cutting point is used to cut the brain tissue edge chain code. In a specific application scene, a horizontal line passing through a chain code cutting point can be drawn, the brain tissue edge chain code is cut into two parts through the horizontal line, each part comprises an upper sagittal sinus, the upper half part is used as an anterior segment upper sagittal sinus chain code, and the lower half part is used as a posterior segment upper sagittal sinus chain code. It should be noted that the target head medical image is generally obtained by scanning a plurality of slices of the brain of the patient, that is, the target head medical image generally includes a plurality of slice images, the brain tissue region may be a brain tissue region corresponding to any slice that needs to be subjected to symmetry axis identification, and then the brain tissue edge chain code corresponding to the slice is analyzed to determine the head symmetry axis of the slice brain tissue region, or the brain tissue region may also be a brain tissue region corresponding to a plurality of specified slices, and the symmetry axis identification of each slice brain tissue region is realized by analyzing the brain tissue edge chain code corresponding to the plurality of specified slices.

102, determining front section candidate angular points according to front section curvature values of all points in the front section upper sagittal sinus chain code, and determining rear section candidate angular points according to rear section curvature values of all points in the rear section upper sagittal sinus chain code;

secondly, for the anterior segment superior sagittal sinus chain code and the posterior segment superior sagittal sinus chain code of a certain fault, respectively finding out an anterior segment candidate angular point and a posterior segment candidate angular point from each point on the anterior segment superior sagittal sinus chain code and the posterior segment superior sagittal sinus chain code, so as to carry out symmetry axis identification by using the candidate angular points in the subsequent process. Taking the determination of candidate corners of the anterior segment as an example, the curvature value of each point in the anterior segment superior sagittal sinus chain code can be calculated according to the corner detection method,

i.e., the anterior segment curvature value, so as to screen the anterior segment candidate corner points according to the anterior segment curvature values of each point. When the anterior curvature value is calculated, one preset anterior chord length parameter or a plurality of preset anterior chord length parameters can be selected. If one is selected, a plurality of points with larger values (or points with values exceeding a preset value) can be used as candidate angular points of the front segment according to the values of the curvature values of the front segment of each point; if a plurality of points are selected, the curvature products of the points can be obtained by multiplying a plurality of front section curvature values of each point, and a plurality of points with larger curvature products (or points with values exceeding a certain value) are used as front section candidate angular points. Through the method, the point at the position with larger bending degree in the anterior segment superior sagittal sinus chain code, namely the anterior segment candidate angular point can be found, as shown in fig. 2, the point at the position with the maximum bending degree of the anterior segment superior sagittal sinus edge and the point at the position with the maximum bending degree of the posterior segment superior sagittal sinus edge are located on the straight line of the head symmetry axis according to the human head structure. And then, finding a rear-segment candidate angular point on the rear-segment upper sagittal sinus chain code by using a similar method so as to screen out a front-segment target angular point and a rear-segment target angular point respectively from the front-segment candidate angular point and the rear-segment candidate angular point in the following process, thereby carrying out symmetry axis identification.

103, detecting a first position feature of the front candidate corner in the front upper sagittal sinus chain code, determining a front target corner based on the first position feature, detecting a second position feature of the rear candidate corner in the rear upper sagittal sinus chain code, and determining a rear target corner based on the second position feature;

further, after determining the anterior candidate corner and the posterior candidate corner for a certain tomographic brain tissue region, a anterior target corner and a posterior target corner, which are finally used for determining the symmetry axis, are selected from the anterior candidate corner and the posterior candidate corner, respectively. In a specific application scenario, taking a front-segment candidate corner point as an example, the concave-convex characteristic of the position of the front-segment candidate corner point on the front-segment superior sagittal sinus chain code, that is, the first position characteristic, that is, whether the front-segment candidate corner point is a concave point or a convex point on the front-segment superior sagittal sinus contour, as shown in fig. 2, according to the characteristic that the intersection point of the symmetry axis and the front-segment superior sagittal sinus edge and the corner point of the symmetry axis and the rear-segment superior sagittal sinus edge should be a concave point, the front-segment candidate corner point is screened, the convex point is removed, only the concave point is reserved, and the front-segment candidate corner point belonging to the concave point is screened as a front-segment target corner point. If the candidate angular points of the previous segment screened in this step include a plurality of angular points, one of the candidate angular points should be selected as a target angular point of the previous segment according to a predetermined rule, for example, an angular point near the center in the previous segment upper sagittal sinus chain code is selected as a target angular point of the previous segment, and for example, an angular point with the largest curvature value is selected as a target angular point of the previous segment. In addition, a back target corner point can be selected from the back candidate corner points in a similar manner as described above.

And 104, determining a head symmetry axis corresponding to the brain tissue region according to a connecting line of the front section target corner point and the rear section target corner point.

Finally, a connecting line of a front section target corner point and a rear section target corner point corresponding to a certain fault can be used as a head symmetrical axis corresponding to the fault, and in addition, in order to realize the rapid identification of the head symmetrical axes corresponding to a plurality of faults, in a specific application scene, a symmetrical axis identification surface can also be fitted by using the connecting lines of the front section target corner points and the rear section target corner points corresponding to the plurality of faults, so that the identification of the head symmetrical axes of all faults can be realized by using the identification surface.

By applying the technical scheme of the embodiment, a brain tissue edge chain code and chain code cutting points are identified from a brain tissue region in a target head medical image, the brain tissue edge chain code is cut into a front section superior sagittal sinus chain code and a rear section superior sagittal sinus chain code according to the chain code cutting points, a front section candidate angular point and a rear section candidate angular point are further found according to curvature values of points of the front section superior sagittal sinus chain code and the rear section superior sagittal sinus chain code, concave points in the candidate angular points are screened based on concave-convex position characteristics of the front section candidate angular point in the front section superior sagittal sinus chain code and the rear section candidate angular point in the rear section superior sagittal sinus chain code, the candidate angular points are respectively used as a front section target angular point and a rear section target angular point, and finally, a head symmetry axis corresponding to the brain tissue region is determined according to a connecting line of the front section target angular point and the rear section target angular point. According to the embodiment of the application, the connecting line of the anterior superior sagittal sinus and the posterior superior sagittal sinus is used as the characteristic of a head symmetry axis, the brain tissue edge chain codes of the brain tissue region outline which is not easily affected by brain tissue lesion and image quality are selected, the superior sagittal sinus characteristic embodied by the brain tissue edge chain codes is used for identifying the anterior segment target angular point and the posterior segment target angular point, the determination of the symmetry axis is realized, the problem of inaccurate identification of the symmetry axis caused by the influence of the brain tissue lesion or the image quality on the tissue definition is solved, the accuracy and the robustness of the head symmetry axis identification are improved, the angular point identification is carried out on one-dimensional data, namely the chain codes, the identification speed is high, and the efficiency is high.

Further, as a refinement and an extension of the specific implementation of the above embodiment, in order to fully illustrate the specific implementation process of the embodiment, another head symmetry axis identification method is provided, as shown in fig. 3, and the method includes:

step 201, acquiring the target head medical image, respectively carrying out contour recognition on the image of each fault, and determining brain tissue areas of a plurality of faults, wherein the target head medical image comprises a CT image and/or an MR image;

in the above embodiment, a CT/MR image of a patient is obtained, and when CT/MR imaging scanning is performed, scanning is generally performed according to a plurality of slices respectively, specifically, CT flat scan (NCCT), CT perfusion imaging (CTP), CT vascular imaging (CTA), MR perfusion imaging (PWI), MR diffusion imaging (DWI), and the like may be selected, and the image needs to include a superior sagittal sinus structure of the brain, which is a main basis for determining the symmetry axis. The brain tissue mask can be extracted through an active contour model algorithm, namely, the mask obtained by removing the skull of an original head portrait and the external tissues of the skull from a certain layer of original image, specifically, the pixel occupied by the tissues in the skull can be marked as 1 through the active contour model algorithm, the rest parts are marked as 0, and the pixel point marked as 1 is taken as the brain tissue area.

Step 202, acquiring a first candidate fault with the largest brain tissue area in a plurality of faults, and taking at least two faults respectively adjacent to the front and the back of the first candidate fault and the first candidate fault as a first target layer; acquiring a second candidate fault with the smallest difference between the area of a brain tissue region in the multiple faults and a preset area, and taking at least two faults respectively adjacent to the front part and the rear part of the second candidate fault and the second candidate fault as a second target layer;

in the above embodiment, in order to realize rapid identification of the symmetry axis of each fault, the first target layer and the second target layer are selected, so that the brain tissue edge chain codes corresponding to the first target layer and the second target layer are analyzed, a symmetry axis identification plane is determined, and head symmetry axis identification is realized. In a specific application scenario, the area of a brain tissue region corresponding to each fault is calculated, and a layer where the brain tissue region with the largest area is located is taken as a first candidate fault. In addition, a second candidate fault with the smallest difference from the preset area is selected from the multiple faults, and the second candidate fault and the two adjacent faults are taken as a second target layer together, where the preset area may be determined by performing statistics on the brain tissue area of each fault, for example, the fault with the area size ranked at the top 10% is taken as the second candidate layer, the preset area may also be determined by a product of the maximum brain tissue area and a specific coefficient, for example, the preset area is 90% of the maximum brain tissue area, the preset area may also be determined according to experience or historical data, and is not limited herein.

Step 203, identifying brain tissue edge chain codes and chain code cutting points of brain tissue areas corresponding to the first target layer and the second target layer, and extracting front section superior sagittal sinus chain codes and rear section superior sagittal sinus chain codes in the brain tissue edge chain codes according to the position relationship between the brain tissue edge chain codes and the chain code cutting points;

optionally, the determining the anterior superior sagittal sinus chain code and the posterior superior sagittal sinus chain code in the brain tissue edge chain code of each target layer in step 203 may specifically include: determining portions of the brain tissue edge chain code above and below a horizontal line corresponding to the chain code cut point as the anterior superior sagittal sinus chain code and the posterior superior sagittal sinus chain code, respectively, wherein the chain code cut point includes a centroid of the brain tissue region; and respectively intercepting the middle parts of the front section upper sagittal sinus chain code and the rear section upper sagittal sinus chain code according to a preset proportion.

In the above embodiment, for each slice in the first and second target layers, the centroid of the brain tissue region is determined first, the centroid is taken as the chain code cut point of the layer, drawing a horizontal line by taking the position of the chain code cutting point as a reference, taking the part above the horizontal line in the brain tissue edge chain code as an anterior segment superior sagittal sinus chain code, taking the part below the horizontal line as a posterior segment superior sagittal sinus chain code, further, because the front section target angular points finally used for identifying the symmetry axis are generally not positioned at the two ends of the front section upper sagittal sinus chain code, and similarly, the rear section target angular points are also generally not positioned at the two ends of the rear section upper sagittal sinus chain code, therefore, in order to reduce the calculation amount, the anterior segment superior sagittal sinus chain code can be cut according to a preset proportion, the middle part is reserved, for example, the middle 70% of the segment is retained as the active portion, and the middle portion of the posterior sagittal sinus code is also truncated.

Step 204, acquiring a plurality of preset front section chord length parameters and a plurality of preset rear section chord length parameters; respectively calculating a plurality of anterior segment curvature values of each point in the anterior segment superior sagittal sinus chain code according to the preset anterior segment chord length parameters, and determining the anterior segment candidate angular point based on curvature products corresponding to the plurality of anterior segment curvature values; respectively calculating a plurality of rear section curvature values of each point in the rear section upper sagittal sinus chain code according to the preset rear section chord length parameters, and determining the rear section candidate angular points based on curvature products corresponding to the rear section curvature values;

in the above embodiment, since the chord length parameter has a large influence on the selection of the subsequent corner point, in order to avoid that the incorrect selection of the chord length parameter has a large influence on the identification of the subsequent symmetry axis, multiple chord length parameters may be respectively selected for the anterior segment and the posterior segment superior sagittal sinus chain codes, for example, 3 preset anterior segment chord length parameters and 3 preset posterior segment chord length parameters are selected, the anterior segment preset chord length parameters are Lf1, Lf2, and Lf3, specifically, Lf 1-10, Lf 2-15, Lf 3-20, and the posterior segment preset chord length parameters are Lb1, Lb2, and Lb3, respectively. Specifically, Lb 1-4, Lb 2-8, and Lb 3-12 may be selected. By using a CPDA corner point detection method, front section curvature values Hf1, Hf2 and Hf3 of each point on the front section sagittal sinus chain code corresponding to different front section preset chord length parameters and rear section curvature values Hb1, Hb2 and Hb3 of each point on the rear section sagittal sinus chain code corresponding to different rear section preset field parameters are respectively obtained, and then a curvature product Hf (Hf 1) Hf2 (Hf 3) corresponding to the front section curvature value of each point on the front section sagittal sinus chain code and a curvature product Hb (Hb 1) Hb2 (Hb 3) corresponding to the rear section curvature value of each point on the rear section sagittal sinus chain code are calculated. And further screening a front section candidate angular point on the front section upper sagittal sinus chain code and a rear section candidate angular point on the rear section upper sagittal sinus chain code respectively according to the Hf and the Hb.

Step 205, detecting a first position feature of the front candidate corner in the front upper sagittal sinus chain code, determining a front target corner based on the first position feature, detecting a second position feature of the back candidate corner in the back upper sagittal sinus chain code, and determining a back target corner based on the second position feature;

optionally, step 205 may specifically include:

step 205-1, respectively determining a preset front section distance corresponding to each preset front section chord length parameter and a preset rear section distance corresponding to each preset rear section chord length parameter;

step 205-2, selecting multiple groups of first reference points which are away from two sides of any front-segment candidate angular point by the preset front-segment distance from the front-segment upper sagittal sinus chain code, wherein each group of first reference points comprises two reference points;

step 205-3, respectively establishing a first straight line and a second straight line which passes through any one of the front-segment candidate corner points and is perpendicular to the first straight line based on a plurality of groups of first reference points, and determining a first position feature of any one of the front-segment candidate corner points as a salient point feature when an intersection point of the first straight line and the second straight line corresponding to at least one group of first reference points is in the brain tissue region;

step 205-4, after deleting the candidate angular points at the front section with the salient point characteristics, screening out the target angular point at the front section with the maximum curvature product;

in the above steps 205-1 to 205-4, candidate corner points are screened according to the concavity and convexity of each candidate corner point corresponding to each layer of image. For a certain anterior segment candidate angular point on the anterior segment superior sagittal sinus chain code, first reference points at preset anterior segment distances are respectively selected at two sides of the anterior segment candidate angular point, wherein the preset anterior segment distances are products of preset anterior segment chord length parameters and preset coefficients, for example, the preset anterior segment distances are 1/2 preset anterior segment chord length parameters, the preset anterior segment chord length parameters correspond to a plurality of preset anterior segment distances, and accordingly, a plurality of groups of first reference points can be obtained. For each group of first reference points, a first straight line L1 passing through the two first reference points is obtained, and a straight line L2 passing through the previous candidate corner point and perpendicular to the straight line L1 is continuously obtained. If the intersection of L1 and L2 is within the brain tissue region, the first location feature of the anterior segment candidate corner point is labeled as a salient point feature. For any one front-segment candidate angular point, as long as the first position characteristic of the front-segment candidate angular point calculated according to a certain preset front-segment chord length parameter is a salient point characteristic, the front-segment candidate angular point is deleted, one with the largest curvature product is further selected from the remaining front-segment candidate angular points to serve as a front-segment target angular point, and a point which belongs to a concave point position and has the largest concave degree in the front-segment upper sagittal sinus chain code corresponding to each layer is determined.

For example, for a candidate point on the chain code, points distant from the chord length parameter 1/2 on both sides of the candidate point are respectively selected, a straight line L1 passing through the two points is obtained, and a straight line L2 perpendicular to the equation L1 passing through the candidate point is continuously obtained. If the intersection point of the straight lines L1 and L2 falls in the brain tissue region of the layer, namely the concave-convex mark of the candidate point under the current chord length parameter is-1, and if not, the concave-convex mark is + 1. Under different chord length parameters, at least one concave-convex mark is-1, namely the candidate point is a convex point, and otherwise, the candidate point is a concave point. Since the superior sagittal sinus should be characterized as a pit on the edge chain code, the pit is preserved according to the labeling. And selecting a concave point with the largest curvature product from the front-back chain code points of each layer as the corresponding angular point of the front-back upper sagittal sinus of the layer.

Step 205-5, selecting multiple groups of second reference points which are away from two sides of any one rear-segment candidate angular point by the preset rear-segment distance from the rear-segment upper sagittal sinus chain code, wherein each group of second reference points comprises two reference points;

step 205-6, respectively establishing a third straight line and a fourth straight line which passes through any one of the back-end candidate corner points and is perpendicular to the third straight line based on a plurality of groups of second reference points, and determining that a second position feature of any one of the back-end candidate corner points is a bump feature when an intersection point of the third straight line and the fourth straight line corresponding to at least one group of second reference points is in the brain tissue region;

and step 205-7, after deleting the candidate corner points of the back section with the salient point characteristics, screening out the target corner points of the back section with the maximum curvature product.

The manner of determining the subsequent target corner point in steps 205-5 to 205-7 is similar to that in steps 205-2 to 205-4, and is not described herein again.

Step 206, filtering the front-segment target angular points according to the relative positions of the front-segment target angular points in the corresponding front-segment upper sagittal sinus chain codes in the first target layer, and filtering the rear-segment target angular points according to the relative positions of the rear-segment target angular points in the corresponding rear-segment upper sagittal sinus chain codes in the first target layer, so that the relative position difference of the front-segment target angular points and the relative position difference of the rear-segment target angular points in the filtered first target layer are both smaller than a preset difference value; filtering the front-section target angular points according to the relative positions of the front-section target angular points in the corresponding front-section upper sagittal sinus chain codes in the second target layer, and filtering the rear-section target angular points according to the relative positions of the rear-section target angular points in the corresponding rear-section upper sagittal sinus chain codes in the second target layer, so that the relative position difference of the front-section target angular points and the relative position difference of the rear-section target angular points in the second target layer after filtering are both smaller than a preset difference value;

step 207, determining a first front section target corner point of the first target layer according to the curvature value of each front section target corner point in the first target layer, and determining a first rear section target corner point of the first target layer according to the curvature value of each rear section target corner point in the first target layer; determining a second front section target corner point of the second target layer according to the curvature value of each front section target corner point in the second target layer, and determining a second rear section target corner point of the second target layer according to the curvature value of each rear section target corner point in the second target layer;

in steps 206 to 207, taking the determination of the first previous-stage target corner point corresponding to the first target layer as an example, the relative positions of the previous-stage target corner points corresponding to the respective faults included in the first target layer in the chain code are respectively obtained, and the previous-stage target corner points are filtered according to the relative positions of all the previous-stage target corner points corresponding to the first target layer. Specifically, the front-segment target corner points with close relative positions are retained, so that the relative position differences of the front-segment target corner points in the retained first target layer are all smaller than the preset difference. And further taking the largest curvature product (the product of curvature products is the product of curvature values corresponding to all the previous-segment chord length parameters) in the remaining previous-segment target corner points as a first previous-segment target corner point af corresponding to the first target layer. Correspondingly, a first rear section target corner ab corresponding to the first target layer, and a second front section target corner bf and a second rear section target corner bb corresponding to the second target layer are calculated in a manner similar to that described above.

Step 208, determining a first connecting line between a first front section target corner point and a first rear section target corner point corresponding to the first target layer and a second connecting line between a second front section target corner point and a second rear section target corner point corresponding to the second candidate group; establishing a symmetry axis identification surface according to the first connecting line and the second connecting line; and acquiring the intersection line of the symmetry axis identification surface and the brain tissue region corresponding to each fault, and determining the intersection line as the head symmetry axis of each fault.

In the above embodiment, a first connecting line La of the first front section target corner af and the first back section target corner ab, and a second connecting line Lb of the second front section target corner bf and the second back section target corner bb are determined, Lb and La are located in the same plane under standard image input, a plane can be determined, and the plane is used as a symmetry axis identification plane. Therefore, two groups of target angular points (namely a first front-section target angular point, a first rear-section target angular point, a second front-section target angular point and a second rear-section target angular point) are determined through analysis of the brain tissue areas of a small number of faults, the head symmetry axes of all the faults are identified, and the symmetry axis identification efficiency is improved.

Further, as a specific implementation of the method in fig. 1, an embodiment of the present application provides a head symmetry axis identification apparatus, as shown in fig. 4, the apparatus includes: