CN111815585A

CN111815585A - Method and system for acquiring coronary tree and coronary entry point based on CT sequence image

Info

Publication number: CN111815585A
Application number: CN202010602490.8A
Authority: CN
Inventors: 王之元; 冯亮; 刘广志; 陈韵岱
Original assignee: Suzhou Runxin Medical Equipment Co ltd
Current assignee: Suzhou Rainmed Medical Technology Co Ltd
Priority date: 2020-06-29
Filing date: 2020-06-29
Publication date: 2020-10-23
Anticipated expiration: 2040-06-29
Also published as: WO2022000727A1; CN111815585B

Abstract

The application provides a method and a system for acquiring a coronary tree and a coronary entry point based on CT sequence images, wherein the method comprises the following steps: acquiring three-dimensional data of a CT sequence image; acquiring the heart gravity center and the vertebra gravity center according to the three-dimensional data; removing lung tissues, descending aorta, spine, ribs, left atrium and left ventricle from the CT three-dimensional image to obtain an interference-free coronary tree image; extracting a coronary tree and a coronary entry point from the interference-free coronary tree image. According to the method, the heart center of gravity and the spine center of gravity are screened out firstly, the positions of the heart and the spine are located, and then lung tissues, descending aorta, the spine, ribs, the left atrium and the left ventricle are removed from the CT image according to the positions of the heart and the spine, so that the operation amount is reduced, the algorithm is simple, the operation is easy, the operation speed is high, the design is scientific, the coronary artery tree and the coronary artery entry point are accurately extracted on the basis of the interference-free coronary artery tree image, and the image processing is accurate.

Description

Method and system for acquiring coronary tree and coronary entry point based on CT sequence image

Technical Field

The invention relates to the technical field of coronary artery medicine, in particular to a method and a system for acquiring a coronary artery tree and a coronary artery entry point based on CT sequence images.

Background

Cardiovascular disease is the leading cause of death in the industrialized world. The main form of cardiovascular disease is caused by the chronic accumulation of fatty substances in the lining layers of the arteries supplying the heart, brain, kidneys and lower limbs. Progressive coronary artery disease restricts blood flow to the heart. Many patients require invasive catheter procedures to assess coronary blood flow due to the lack of accurate information provided by current non-invasive tests. Therefore, there is a need for a non-invasive method of quantifying blood flow in human coronary arteries to assess the functional significance of a possible coronary artery disease. A reliable assessment of arterial volume would therefore be important for treatment planning that addresses patient needs. Recent studies have demonstrated that hemodynamic characteristics, such as Fractional Flow Reserve (FFR), are important indicators for determining optimal treatment for patients with arterial disease. Conventional evaluation of fractional flow reserve uses invasive catheterization to directly measure blood flow characteristics such as pressure and flow rate. However, these invasive measurement techniques present risks to the patient and can result in significant costs to the healthcare system.

Computed tomography arterial angiography is a computed tomography technique for visualizing arterial blood vessels. For this purpose, a beam of X-rays is passed from a radiation source through a region of interest in the body of a patient to obtain projection images.

The CT data in the prior art are not screened, so that the calculation amount is large, and the problems of low calculation speed and inaccurate calculation exist.

Disclosure of Invention

The invention provides a method and a system for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, which aim to solve the problem of how to accurately extract a blood vessel central line.

To achieve the above object, in a first aspect, the present application provides a method for acquiring a coronary tree and a coronary access point based on CT sequence images, comprising:

acquiring three-dimensional data of a CT sequence image;

acquiring the heart gravity center and the vertebra gravity center according to the three-dimensional data;

removing lung tissues, descending aorta, spine, ribs, left atrium and left ventricle from the CT three-dimensional image to obtain an interference-free coronary tree image;

extracting a coronary tree and a coronary entry point from the interference-free coronary tree image.

Optionally, the above method for acquiring a coronary tree and a coronary access point based on CT sequence images, the method for acquiring a center of gravity of a heart from the three-dimensional data includes:

drawing a gray level histogram of the CT image;

sequentially acquiring M point to M-1 point and M point to M-2 point along the direction from the end point M to the original point O of the gray histogram until the volume of each gray value area from the M point to the O point is acquired;

acquiring the volume ratio V of the volume of each gray value area to the volume of the total area from the M point to the O point;

if V is equal to b, picking up a starting point corresponding to the gray value region, projecting the starting point onto the CT three-dimensional image to obtain a heart region three-dimensional image, and picking up a physical gravity center of the heart region three-dimensional image, namely the heart gravity center P₂；

Wherein b represents a constant, 0.2 < b < 1.

Optionally, in the above method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, the method for acquiring a vertebral center of gravity from the three-dimensional data includes:

if V is a, picking up a starting point corresponding to the gray value region, projecting the starting point onto the CT three-dimensional image to obtain a bone region three-dimensional image, and picking up a physical gravity center of the bone region three-dimensional image, namely a vertebral gravity center P₁；

Wherein a represents a constant, 0 < a < 0.2.

Optionally, the method for acquiring a coronary tree and a coronary access point based on a CT sequence image comprises:

setting a lung gray threshold Q according to medical knowledge and a CT image imaging principle_{Lung (lung)}；

If the gray value in the gray histogram is less than Q_{Lung (lung)}And removing the image corresponding to the gray value to obtain the first image with the lung tissue removed.

Optionally, the above method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, the method for removing a descending aorta based on the CT three-dimensional image comprises:

the center of gravity P of the heart₂Projecting the first image to obtain the center O of the heart₁；

Setting the descending aorta gray threshold Q_DescendCarrying out binarization processing on the first image;

according to the descending aorta and the heart center O₁And the center O of the spine and the heart₁Obtaining a circle corresponding to the descending aorta;

removing the descending aorta from the first image, resulting in the second image.

Optionally, the above method for acquiring coronary tree and coronary access point based on CT sequence image sets the descending aorta gray threshold Q_DescendThe method for performing binarization processing on the first image comprises the following steps:

acquiring the gray value in the first image larger than the descending aorta gray threshold value O_DescendCalculating the average gray value of the pixel point PO

Slicing the bottom layer of the first image in a layering mode to obtain a first two-dimensional slice image group;

according to

To the firstCarrying out binarization processing on an image, removing impurity points in the first image to obtain a binarized image, wherein k is a positive integer and Q is_kThe gray value corresponding to the kth pixel point PO is represented, and p (k) represents the pixel value corresponding to the kth pixel point PO.

Optionally, the method for acquiring coronary tree and coronary access point based on CT sequence image as described above is based on the descending aorta and heart center O₁And the center O of the spine and the heart₁The method for obtaining the circle corresponding to the descending aorta comprises the following steps:

setting a threshold value r for the radius of the circle formed by the descending aorta to the edge of the heart_Threshold(s)；

Acquiring an approximate region of the spine and an approximate region of the descending aorta according to the fact that the distance between the descending aorta and the heart is smaller than the distance between the spine and the heart;

and removing error pixel points according to the approximate area of the descending aorta to obtain the descending aorta image, namely the circle corresponding to the descending aorta.

Optionally, the above method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, wherein the method for acquiring an approximate region of the spine and an approximate region of the descending aorta comprises:

if the radius r of the circle obtained by the Hough detection algorithm is more than r_Threshold(s)If the circle is a circle corresponding to the spine, the center and the radius of the circle are not recorded, and the circle is the approximate region of the spine;

if the radius r of the circle obtained by the Hough detection algorithm is less than or equal to r_Threshold(s)Then the circle may be the circle corresponding to the descending aorta, and the center and radius of the circle are recorded, i.e. the approximate area of the descending aorta.

Optionally, in the method for obtaining a coronary tree and a coronary artery entry point based on a CT sequence image, the method for removing error pixel points according to the approximate region of the descending aorta to obtain the descending aorta image, that is, the circle corresponding to the descending aorta includes:

and screening the circle center and the radius of the circle in the approximate area of the descending aorta, removing the circle with the larger deviation of the circle center between the adjacent sections, namely removing error pixel points, forming a seed point list of the descending aorta, and obtaining the descending aorta image, namely the circle corresponding to the descending aorta.

Optionally, in the method for acquiring a coronary tree and a coronary artery access point based on a CT sequence image, the method for removing a descending aorta from the first image to obtain the second image includes:

if the number of circle centers in the seed point list is more than or equal to 3, calculating the average radius of all the seed points

And the mean center point P₃；

Calculate by P₃As a center of circle, in

Is the average value of gray values of all pixel points PO in a circle with a radius

Setting a parameter a to obtain a connected domain gray threshold

Wherein a is a positive number;

recalculating the center point P of the connected component₄；

Sequentially calculating P on each layer of two-dimensional slices from the bottom layer₃And P₄The Euclidean distance of;

if P is on said two-dimensional slice of the b-th layer₃And P₄Setting the pixel value corresponding to the pixel point PO of the layer b and above all the two-dimensional slices to be 0 if the Euclidean distance of the layer b is more than m, and obtaining the images corresponding to the layer b-1 from the first layer to the layer b, wherein b is a positive number more than or equal to 2 and m is more than or equal to 5;

if said two dimensions of the b-th layerSlice P₃And P₄If the Euclidean distance is less than or equal to m, extracting pixel points with the gray value greater than 0 in the two-dimensional slice of the b-th layer, and adding P on the two-dimensional slice of the b-th layer₃Point set to P₄Point; and setting the pixel value corresponding to the pixel point PO of all the two-dimensional slices on the (b + 1) th layer and above as 0 to obtain the images corresponding to the first layer to the (b) th layer, which are the second images.

Optionally, the above method for acquiring a coronary tree and a coronary artery access point based on a CT sequence image, the method for removing a vertebra according to the CT three-dimensional image includes:

according to the vertebral center of gravity P₁Heart center of gravity P₂Setting a spinal gray level threshold Q_Ridge；

Extracting gray value greater than Q corresponding to pixel point PO in the second image_RidgeThe pixel point of (2);

and extracting a vertebra connected domain according to the extracted pixel points, and removing the vertebra connected domain to obtain a third image.

Optionally, the method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image includes:

extracting pixel points of which the gray values corresponding to the pixel points PO are greater than 0 in the third image, and setting the gray values of the pixel points corresponding to the second image to be 0 to obtain a fourth image;

setting rib gray threshold Q_RibsExtracting a gray value Q > Q from the fourth image_RibsAnd extracting a rib connected domain according to the extracted pixel points, removing the rib connected domain, and obtaining a fifth image from which the descending aorta, the spine and the ribs are removed, wherein the fifth image is an image containing the left ventricle, the left atrium and an interference-free coronary tree.

Optionally, the method for acquiring a coronary tree and a coronary access point based on a CT sequence image as described above, the method for removing the left atrium and left ventricle from the CT three-dimensional image includes:

extracting an aorta centerline from the fifth image;

obtaining a sixth image along a vertical plane of the centerline of the aorta according to the trend of the centerline of the aorta;

obtaining a connected domain image of the left atrium and left ventricle;

and removing the connected domain image of the left atrium and the left ventricle from the fifth image to obtain an interference-free coronary tree image, namely a tenth image.

Optionally, the above method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, the method for extracting an aorta centerline from the fifth image comprises:

carrying out layered slicing on the fifth image to obtain a binarization image group;

obtaining a circle center P from each layer of the slices in the binarization image group_5kAnd the corresponding circle has a radius R_kK denotes a k-th layer slice;

filtering circle center P_5kGenerating a new point list;

radius of filtration R_kGenerating a new radius list;

and corresponding the pixel points in the point list and the radius list in each layer of the slice into the fifth image, and obtaining the aorta central line in the fifth image.

Optionally, in the method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, the method for hierarchically slicing the fifth image to obtain a binarized image group includes:

A) slicing the top layer of the fifth image in a layering mode to obtain a second two-dimensional image group;

B) setting a coronary Tree Gray threshold Q_{Crown 1}(ii) a According to

Carrying out binarization processing on the slices of each layer of the fifth image, and removing impurity points in the fifth image to obtain a binarization image group;

wherein m is a positive integer, Q_mRepresents the m-th imageThe gray value corresponding to the pixel point PO, p (m), represents the pixel value corresponding to the mth pixel point PO.

Optionally, in the above method for acquiring coronary artery tree and coronary artery entry point based on CT sequence image, the circle center P is obtained from each layer of the slice in the binarized image group_5kAnd the corresponding circle has a radius R_kThe method comprises the following steps:

C) establishing a search engine list for each layer of the slices in the binarization image group, wherein the search engine list comprises: a point list and a radius list, wherein the point corresponding to the pixel value 1 extracted from the fifth image is filled into the point list of each layer of the slice;

D) setting the number threshold of pixel points in the point list of each layer of the slice to be N_{Threshold 1}、N_{Threshold 2}And the radius threshold is R_{Threshold 1}、R_{Threshold 2}Sequentially carrying out the processes from the step E to the step M on each layer of the slices from the top layer;

E) if N is present_k≤N_{Threshold 1}，R_k＝R_{Threshold 1}M, wherein Nk represents the number of pixel points in the point list of the k-th slice, 1 circle in the k-th slice is detected, and the center of the circle is taken as the center O of the circle_kStep I is carried out, and if no circle is detected, step H is carried out;

F) if N is present_k≤N_{Threshold 1}，R_k≠R_{Threshold 1}Detecting 3 circles in the k layer slice, if 3 circles are detected, performing step I, and if 3 circles are not detected, performing step H; preferably, m is 0 to 0.5.

G) If N is present_k＞N_{Threshold 1}If the circle center is determined again, the point with the circle center in the k-1 layer slice and the closest distance to the tail point D in the point list is taken as the circle center O_kStep I is carried out, and if no circle is detected, step H is carried out; preferably, N_{Threshold 1}＝4。

H) Detecting N_kAnd N_{Threshold 1}-1, repeating said steps E to G, if no circle has been detected yet, detecting N and N_{Threshold 1}-2 relation, repeatThe steps E to G; and so on until finding the center of a circle O_k；

I) With the center of circle O_kAs a starting point, 3 points with the gray value of 0 are respectively found along the positive direction, the negative direction and the positive direction of the X axis; determining a circle according to the 3 points to find the circle center P_5kAnd a radius R_k。

Optionally, in the above method for acquiring a coronary tree and a coronary entry point based on a CT sequence image, the center P of the circle is filtered_5kThe method for generating the new point list comprises the following steps:

J) if the radius R of the k layer slice_k＜R_{Threshold 2}Repeating the process from the step E to the step I until the radius R is found_k≥R_{Threshold 2}Center of circle P of_5k；

K) If the center of the circle P₅k is less than 0 on the fifth image, the process from the step E to the step I is repeated until the radius R is found₁≥R_{Threshold 2}And the gray value is greater than or equal to the circle center P of 0_5k；

L) reacting R with₁≥R_{Threshold 2}And the gray value is greater than or equal to the circle center P of 0_5kAdding the entry point list to generate a new radius list, radius R_kAdd into the radius list.

Optionally, the above-described method of acquiring a coronary tree and a coronary entry point based on CT sequence images, the filtering radius R_kThe method for generating the new radius list comprises the following steps:

m) if N_k＜N_{Threshold 2}Then compare the center of circle P_5kDistance L from the last point in the point list, if L > L_Threshold(s)Repeating the steps E to N until the number N of the points in the point list_k≥N_{Threshold 2}Or L is less than or equal to L_Threshold(s)；

N) if N_k≥N_{Threshold 2}Or N is_k＜N_{Threshold 2}、L≤L_Threshold(s)Will deviate from the center P_5kReplacing the radius value of the far point with the average radius value of the rest points as Rk, and filling the radius Rk into the radius listAnd generating a new radius list.

Optionally, the method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image as described above, wherein the method for obtaining the sixth image along a vertical plane of the centerline of the aorta according to the trend of the centerline of the aorta comprises:

setting left ventricular grayscale threshold Q_{Left side of}Intercepting an XZ plane of a tail point D in the point list in the fifth image to acquire a gray value Q & gtQ_{Left side of}Obtaining the center O of a circle formed by all the pixel points₂The center of the circle O₂Projecting the image on the fifth image to obtain the gravity center point P of the left ventricle₆；

Obtaining the circle center P in the XZ plane_5kAnd a center of gravity point P₆A perpendicular bisector of the line, the perpendicular bisector and the Y axis forming a plane;

and setting the pixel value of a pixel point on the fifth image in the plane formed by the perpendicular bisector and the Y axis as 0 to obtain a sixth image.

Optionally, the method for acquiring a coronary tree and a coronary access point based on a CT sequence image includes:

picking up a starting point, a middle point and a gravity point P in the point list₆And drawing a Bezier curve at the end point;

assuming a center point P of the connected domain₄The tail point D is positioned on the Bessel curve, and the tail point D and the gravity center point P are picked up₆A curve segment of the Bezier curve in between, along the end point D to the P₆Extending in a direction of R_{Threshold 1}Acquiring an extension section curve;

if the pixel points in the extension section curve are positioned in the fifth image and the gray value Q of the pixel points in the extension section curve is more than Q_DescendExtracting the pixel points to obtain the aorta image, namely a seventh image;

subtracting the seventh image from the fifth image to obtain an eighth image;

picking up a center of gravity point P of the left ventricle within the eighth image₆Peripheral gradation value Q > Q_DescendAnd obtaining a connected domain image of the left atrium and the left ventricle, namely a ninth image.

Optionally, the method for acquiring coronary tree and coronary access point based on CT sequence image as described above, wherein the method for extracting coronary tree and coronary access point from the non-interference coronary tree image comprises:

acquiring a connected domain image of the coronary artery tree from the tenth image, wherein the connected domain image is an eleventh image;

acquiring a coronary artery tree image according to the eleventh image;

extracting a coronary entry point from the coronary tree image.

Optionally, in the method for acquiring a coronary tree and a coronary artery entry point based on a CT sequence image, the method for acquiring a connected component image of a coronary tree from the tenth image, that is, an eleventh image, and acquiring a coronary tree image according to the eleventh image includes:

setting a grayscale threshold Q_{Crown 2}Extracting a gray value Q > Q in the tenth image_{Crown 2}Obtaining a connected domain image of the coronary artery tree, namely an eleventh image;

and subtracting the seventh image from the eleventh image to obtain a coronary artery tree image.

extracting two areas with the largest area of a top connected domain from the coronary tree image, namely an image with a left coronary ostium and a right coronary ostium;

and performing expansion processing on the seventh image to obtain an area image which is overlapped with the image with the left coronary ostium and the right coronary ostium, namely the coronary artery entry point.

In a second aspect, the present application provides a computer storage medium, a computer program being executed by a processor for implementing the above-mentioned method for acquiring a coronary tree and a coronary access point based on a CT sequence image.

In a third aspect, the present application provides a system for acquiring a coronary tree based on CT sequence images, comprising: a CT data acquisition device, a heart gravity center extraction device, a vertebra gravity center extraction device, an interference-free coronary artery tree image acquisition device, a coronary artery tree extraction device and a coronary artery entry point extraction device;

the CT data acquisition device is used for acquiring three-dimensional data of a CT sequence image;

the heart gravity center extraction device is connected with the CT data acquisition device and is used for acquiring the heart gravity center according to the three-dimensional data;

the spine gravity center extraction device is connected with the CT data acquisition device and is used for acquiring the spine gravity center according to the three-dimensional data;

the non-interference coronary artery tree image acquisition device is connected with the CT data acquisition device, the heart gravity center extraction device and the vertebra gravity center extraction device and is used for removing lung tissues, descending aorta, spines, ribs, left atrium and left ventricle from the CT three-dimensional image to obtain a non-interference coronary artery tree image;

the coronary artery tree extraction device is connected with the non-interference coronary artery tree image acquisition device and is used for extracting a coronary artery tree from the non-interference coronary artery tree image;

the coronary artery entry point extraction device is connected with the coronary artery tree extraction device and used for extracting a coronary artery entry point from the coronary artery tree.

The beneficial effects brought by the scheme provided by the embodiment of the application at least comprise:

the method comprises the steps of screening out the center of gravity of the heart and the center of gravity of the spine, positioning the positions of the heart and the spine, removing lung tissues, descending aorta, the spine, ribs, the left atrium and the left ventricle from a CT image according to the positions of the heart and the spine, reducing the operation amount, being simple in algorithm, easy to operate, high in operation speed, scientific in design, capable of accurately extracting the coronary artery tree and the coronary artery entry point on the basis of an interference-free coronary artery tree image, and accurate in image processing.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:

FIG. 1 is a flow chart of a method of acquiring a coronary tree and coronary access points based on CT sequence images according to the present application;

FIG. 2 is a diagram of the present application for obtaining the center of gravity P of the heart₂A flow chart of the method of (1);

FIG. 3 is a flow chart of a method of removing lung tissue according to the present application;

FIG. 4 is a flow chart of a method of removing a descending aorta of the present application;

FIG. 5 is a flowchart of S3040 of the present application;

FIG. 6 is a flow chart of S3050 of the present application;

FIG. 7 is a flowchart of S3060 of the present application;

FIG. 8 is a flow chart of a method of removing a spine according to the present application;

FIG. 9 is a flow chart of a method of removing ribs;

FIG. 10 is a flow chart of a method of removing the left atrium and left ventricle;

FIG. 11 is a flowchart of S3130 of the present application;

fig. 12 is a flowchart of S3140 of the present application;

fig. 13 is a flowchart of S4000 of the present application;

fig. 14 is a flowchart of S4200 of the present application;

FIG. 15 is a block diagram of a system for acquiring coronary tree and coronary access points based on CT sequence images according to the present application;

FIG. 16 is a schematic diagram of a first image of the present application;

FIG. 17 is a schematic diagram of a second image of the present application;

FIG. 18 is a schematic structural diagram of a third image of the present application;

FIG. 19 is a schematic diagram of a fifth image of the present application;

FIG. 20 is a schematic structural diagram of a sixth image of the present application;

FIG. 21 is a schematic diagram of a seventh image of the present application;

FIG. 22 is a schematic structural diagram of an eighth image of the present application;

FIG. 23 is a schematic diagram of a ninth image of the present application;

FIG. 24 is a schematic diagram of a tenth image of the present application;

FIG. 25 is a schematic diagram of an eleventh image of the present application;

the following reference numerals are used for the description:

a CT data acquisition device 100, a heart gravity center extraction device 200, a spine gravity center extraction device 300, an interference-free coronary artery tree image acquisition device 400, a coronary artery tree extraction device 500 and a coronary artery entry point extraction device 600.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the specific embodiments of the present invention and the accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the following description, for purposes of explanation, numerous implementation details are set forth in order to provide a thorough understanding of the various embodiments of the present invention. It should be understood, however, that these implementation details are not to be interpreted as limiting the invention. That is, in some embodiments of the invention, such implementation details are not necessary. In addition, some conventional structures and components are shown in simplified schematic form in the drawings.

CT data in the prior art are not screened, so that the calculation amount is large, and the problems of low calculation speed and inaccurate calculation exist.

Example 1:

in order to solve the above problem, the present application provides a method for acquiring a coronary tree and a coronary access point based on a CT sequence image, as shown in fig. 1, comprising:

s1000, acquiring three-dimensional data of the CT sequence image, wherein the three-dimensional data comprises:

s2000, acquiring the center of gravity of the heart and the center of gravity of the spine according to the three-dimensional data;

(1) as shown in FIG. 2, the center of gravity P of the heart is obtained₂The method comprises the following steps:

s2100, drawing a gray level histogram of the CT image;

s2200, sequentially acquiring M to M-1 and M-2 points along the direction from the end point M to the original point O of the gray histogram until the volume of each gray value area from the M to the O is acquired;

s2300, acquiring the volume ratio V of the volume of each gray value area to the volume of the total area from the M point to the O point;

s2400, if V is equal to b, picking up a starting point corresponding to the gray value region, projecting the starting point onto the CT three-dimensional image to obtain a heart region three-dimensional image, and picking up a physical gravity center of the heart region three-dimensional image, namely the heart gravity center P₂(ii) a Wherein b represents a constant, 0.2 < b < 1. Preferably, 0.4 < b < 1, with b 0.6 being the most effective.

(2) Obtaining the center of gravity P of the spine₁The method comprises the following steps:

if V is a, picking up a starting point corresponding to the gray value region, projecting the starting point onto the CT three-dimensional image to obtain a bone region three-dimensional image, and picking up the physical gravity center of the bone region three-dimensional image, namely the vertebral gravity center P₁(ii) a Wherein a represents a constant, 0 < a < 0.2. Preferably, 0 < a < 0.1, with a being 0.005 for best results.

S3000, removing lung tissues, descending aorta, spines, ribs, left atrium and left ventricle from the CT three-dimensional image to obtain an interference-free coronary tree image;

I) as shown in fig. 3, a method of removing lung tissue, comprising:

s3010, setting a lung gray threshold Q according to medical knowledge and CT image imaging principle_{Lung (lung)}；

S3020, if the gray level value in the gray level histogram is less than Q_{Lung (lung)}Then the image corresponding to the gray value is removed, resulting in the first image shown in fig. 16 with the lung tissue removed. Preferably, Q_{Lung (lung)}＝-150～-50，Q_{Lung (lung)}Best results are obtained when the ratio is-100.

II) as shown in fig. 4, a method of removing a descending aorta, comprising:

s3030, shifting the center of gravity P of the heart₂Projecting the first image to obtain the center O of the heart₁；

S3040, setting descending aorta gray threshold Q_DescendCarrying out binarization processing on the first image; preferably, Q_Descend＝150～240，Q_DescendThe best effect is 200, as shown in fig. 5, comprising:

s3041, acquiring a pixel point PO with the gray value in the first image larger than the descending aorta gray threshold Qdown, and calculating the average gray value of the pixel point PO;

s3042, slicing the bottom layer of the first image in a layering mode to obtain a first two-dimensional slice image group;

s3043 according to

And carrying out binarization processing on the first image, removing impurity points in the first image, and obtaining a binarized image, wherein k is a positive integer, Qk represents a gray value corresponding to the kth pixel point PO, and P (k) represents a pixel value corresponding to the kth pixel point PO. Preferably, Q_{Crown 1}＝150～220，Q_{Crown 1}The best effect is 200.

S3050, according to the descending aorta and the center O of the heart₁And the center O of the spine and heart₁The corresponding circle of the descending aorta is obtained, as shown in fig. 6, and includes:

s3051, setting a radius threshold of a circle formed by descending aorta to the edge of the heart to be r_Threshold(s)(ii) a Preferably, r_Threshold(s)＝5～15；

S3052, acquiring an approximate region of the spine and an approximate region of the descending aorta according to that a distance between the descending aorta and the heart is smaller than a distance between the spine and the heart, including:

(1) if the radius r of the circle obtained by the Hough detection algorithm is more than r_Threshold(s)If the circle is a circle corresponding to the spine, the center and the radius of the circle are not recorded, and the circle is the approximate region of the spine;

(2) if the radius r of the circle obtained by the Hough detection algorithm is less than or equal to r_Threshold(s)Then the circle may be the circle corresponding to the descending aorta, and the center and radius of the circle are recorded, i.e. the approximate area of the descending aorta.

S3053, removing error pixel points according to the approximate region of the descending aorta to obtain an descending aorta image which is a circle corresponding to the descending aorta, and the method comprises the following steps:

and screening the circle center and the radius of the circle in the approximate area of the descending aorta, removing the circle with the larger deviation of the circle center between the adjacent slices, namely removing error pixel points, forming a seed point list of the descending aorta, and obtaining an image of the descending aorta, namely the circle corresponding to the descending aorta.

S3060, removing the descending aorta from the first image to obtain a second image as shown in fig. 17, as shown in fig. 7, including:

s3061, if the number of circle centers in the seed point list is more than or equal to 3, calculating the average radius of all the seed points

And the mean center point P₃；

S3062, calculating as P₃As a center of circle, in

Setting a parameter a to obtain a connected domain gray threshold

Wherein a is a positive number; preferably, the effect is best when the a is 20-40 and the a is 30.

S3063, heavyNew computation of center point P of connected domain₄；

S3064, sequentially calculating P on each layer of two-dimensional slices from the bottom layer₃And P₄The Euclidean distance of;

s3065, if P is on the two-dimensional slice of the b-th layer₃And P₄If the Euclidean distance is greater than m, setting the pixel value corresponding to the pixel point PO of all the two-dimensional slices on the b-th layer and above to be 0, and obtaining an image corresponding to the first layer to the b-1-th layer, wherein b is a positive number greater than or equal to 2, and m is greater than or equal to 5;

s3066, if P is on the two-dimensional slice of the b-th layer₃And P₄If the Euclidean distance is less than or equal to m, extracting pixel points with the gray value greater than 0 in the two-dimensional slice of the b-th layer, and adding P on the two-dimensional slice of the b-th layer₃Point set to P₄Point; and setting the pixel value corresponding to the pixel point PO of all the two-dimensional slices on the (b + 1) th layer and above as 0 to obtain the images corresponding to the first layer to the (b) th layer, namely a second image.

III) as shown in fig. 8, a method of removing a vertebra, comprising:

s3070, according to the vertebral center of gravity P₁Heart center of gravity P₂Setting a spinal gray level threshold Q_Ridge；

S3080, extracting gray values corresponding to the pixel points PO in the second image to be larger than Q_RidgeThe pixel point of (2);

s3090, extracting a vertebra connected domain according to the extracted pixel points, and removing the vertebra connected domain to obtain a third image shown in fig. 18.

IV) as shown in fig. 9, a method of removing ribs, comprising:

s3100, extracting a pixel point having a gray value larger than 0 corresponding to the pixel point PO in the third image, and setting the gray value of the pixel point corresponding to the second image to 0 to obtain a fourth image (which can be understood by those skilled in the art and is therefore omitted);

s3110, setting rib gray threshold Q_RibsExtracting gray value Q > Q from the fourth image_RibsExtracting a rib connected domain according to the extracted pixel points, removing the rib connected domain to obtain a signal for removing the descending aorta,Fig. 19 shows a fifth image of the spine and ribs, which is an image of an undisturbed coronary tree including the left ventricle, the left atrium, and the left atrium. Preferably, Q_Ribs＝10～40，Q_RibsThe best results are 30.

(V) as shown in fig. 10, a method of removing the left atrium and left ventricle comprises:

s3120, extracting an aorta centerline from the fifth image, including:

s3121, carrying out layered slicing on the fifth image to obtain a binarization image group, wherein the binarization image group comprises:

B) setting a coronary Tree Gray threshold Q_{Crown 1}(ii) a According to

wherein m is a positive integer, Q_mThe gray value corresponding to the mth pixel point PO is represented, and p (m) represents the pixel value corresponding to the mth pixel point PO.

S3122, obtaining circle center P from each layer of slices in the binary image group_5kAnd the corresponding circle has a radius R_kAnd k denotes a k-th layer slice including:

C) establishing a search engine list for each layer of slices in the binary image group, wherein the search engine list comprises the following steps: the point list and the radius list are used for filling the point corresponding to the pixel value 1 extracted from the fifth image into the point list of each layer of slice;

D) setting the number threshold of the pixel points in the point list of each layer of slices to be N_{Threshold 1}、N_{Threshold 2}And the radius threshold is R_{Threshold 1}、R_{Threshold 2}Sequentially carrying out the processes from the step E to the step M on each layer of slices from the top layer; preferably, R_{Threshold 1}＝15mm，R_{Threshold 2}＝3mm。

E) If N is present_k≤N_{Threshold 1}，R_k＝R_{Threshold 1}M, where Nk represents the k-th layerDetecting 1 circle in the kth layer of slices by taking the center of the circle as the center O of the circle_kStep I is carried out, and if no circle is detected, step H is carried out;

F) if N is present_k≤N_{Threshold 1}，R_k≠R_{Threshold 1}Detecting 3 circles in the k layer slice, if 3 circles are detected, performing a step I, and if 3 circles are not detected, performing a step H; preferably, m is 0 to 0.5.

G) If N is present_k＞N_{Threshold 1}Determining the circle center again, and taking the point with the circle center in the k-1 layer slice and the point with the closest distance from the last point D in the point list as the circle center O_kStep I is carried out, and if no circle is detected, step H is carried out; preferably, N_{Threshold 1}＝4。

H) Detecting N_kAnd N_{Threshold 1}-1, repeating steps E to G, if no circle has been detected yet, detecting N and N_{Threshold 1}-2, repeating steps E to G; and so on until finding the center of a circle O_k；

I) Around the center O_kAs a starting point, 3 points with the gray value of 0 are respectively found along the positive direction, the negative direction and the positive direction of the X axis; determining a circle according to the 3 points to find the circle center P_5kAnd a radius R_k。

S3123, filtering the circle center P_5kGenerating a new point list, comprising:

J) if the radius R of the k-th slice_k＜R_{Threshold 2}Repeating the process from step E to step I until the radius R is found_k≥R_{Threshold 2}Center of circle P of_5k；

S3124, filtering radius R_kGenerating a new radius list, comprising:

m) if N_k＜N_{Threshold 2}Then compare the center of circle P_5kDistance L from the last point in the point list, if L > L_Threshold(s)Repeating the steps E to N until the number N of the points in the point list_k≥N_{Threshold 2}Or L is less than or equal to L_Threshold(s)(ii) a Preferably, L_Threshold(s)＝8mm。

N) if N_k≥N_{Threshold 2}Or N is_k＜N_{Threshold 2}、L≤L_Threshold(s)Will deviate from the center P_5kThe radius value of the far point is replaced by the average radius value of the remaining points as R_kRadius R_kAnd filling the radius list to generate a new radius list. Preferably, N_{Threshold 2}＝3。

And S3125, corresponding the pixel points in the point list and the radius list in each slice to a fifth image, and obtaining the aorta central line in the fifth image.

S3130, segmenting the fifth image into two parts along a vertical plane of the centerline of the aorta according to the trend of the centerline of the aorta, and taking an image of the bottom of the vertical plane as a sixth image as shown in fig. 20, as shown in fig. 11, including:

s3131, setting a left ventricle gray level threshold Q_{Left side of}Intercepting an XZ plane of a tail point D in the point list in the fifth image to acquire a gray value Q & gtQ_{Left side of}Obtaining the center O of a circle formed by all the pixel points₂Centering on the center O₂Projecting the image on a fifth image to obtain a gravity center point P of the left ventricle₆；

S3132, obtaining a circle center P in the XZ plane_5kAnd a center of gravity point P₆The perpendicular bisector of the straight line, and the plane formed by the perpendicular bisector and the Y axis;

s3133, setting a pixel value of a pixel point on the fifth image in a plane formed by the perpendicular bisector and the Y axis to 0, and obtaining a sixth image.

S3140, obtaining a connected domain image of the left atrium and the left ventricle, as shown in fig. 12, including:

s3141, pick up the starting point, the middle point and the gravity point P in the point list₆And drawing a Bezier curve at the end point;

s3142, assuming the center point P of the connected domain₄The tail point D is positioned on the Bessel curve, and the tail point D and the gravity center point P are picked up₆Curve segment of a bezier curve in between, along end points D to P₆Extending in a direction of R_{Threshold 1}Acquiring an extension section curve;

s3143, if the pixel point in the extension section curve is positioned in the fifth image and the gray value Q of the pixel point in the extension section curve is larger than Q_DescendThen, the pixel points are extracted to obtain an aorta image, which is the seventh image shown in fig. 21, including: obtaining the corresponding layer of the image in the fifth image, and according to the point list and the radius data, the distance between the circle center of the corresponding layer is smaller than the radius and the gray scale Q is obtained>Extracting pixels of Q drop to obtain image pixel values, expanding the image, multiplying the expanded image by a sixth image, replacing each pixel value to 0-255 by adopting a Sigmoid nonlinear function, calculating the gradient of each pixel, extracting pixel points with gradient values larger than 10, setting the pixel values of the corresponding pixel points in the sixth image to 0, and then extracting a connected domain with the pixel values larger than 200 by taking a center line point list as a seed point to obtain a seventh image; preferably, Q_Descend＝180～220，Q_DescendThe best effect is 200.

S3144, subtracting the seventh image from the fifth image to obtain an eighth image shown in FIG. 22;

s3145, in the eighth image, the gravity center point P of the left ventricle is picked up₆Peripheral gradation value Q > Q_DescendThe connected domain image of the left atrium and the left ventricle is obtained from the pixel points of (1), which is the ninth image shown in fig. 23.

S3150, the ninth image, that is, the tenth image shown in fig. 24 is removed from the fifth image.

S4000, extracting a coronary artery tree and a coronary artery entry point from the interference-free coronary artery tree image, as shown in fig. 13, including:

s4100, obtaining a connected component image of the coronary artery tree from the tenth image, i.e. the eleventh image shown in FIG. 25An image; acquiring a coronary tree image according to the eleventh image, wherein the coronary tree image comprises: setting a grayscale threshold Q_{Crown 2}Extracting gray value Q > Q in the tenth image_{Crown 2}Obtaining a connected domain image of the coronary artery tree, namely an eleventh image; q_{Crown 2}The average gray value-x of each pixel point in the point list is 50-150, and the best effect is achieved when x is 100.

S4200, subtracting the seventh image from the eleventh image to obtain a coronary artery tree image; extracting coronary entry points from the coronary tree image, as shown in fig. 14, includes:

s4210, extracting two areas with the largest top connected area from the coronary tree image, namely the image with a left coronary ostium and a right coronary ostium;

s4220, performing dilation processing on the seventh image to obtain an image of a region overlapping with the image of the left coronary ostium and the image of the right coronary ostium, i.e. a coronary entry point.

Example 2:

as shown in fig. 15, the present application provides a system for acquiring a coronary tree based on CT sequence images, comprising: a CT data acquisition device 100, a heart center of gravity extraction device 200, a spine center of gravity extraction device 300, an interference-free coronary tree image acquisition device 400, a coronary tree extraction device 500, and a coronary entry point extraction device 600; the CT data acquisition device 100 is used for acquiring three-dimensional data of CT sequence images; the heart gravity center extraction device 200 is connected with the CT data acquisition device 100 and is used for acquiring the heart gravity center according to the three-dimensional data; a vertebral center of gravity extraction device 300 connected to the CT data acquisition device 100 for acquiring a vertebral center of gravity from the three-dimensional data; the non-interference coronary artery tree image acquisition device 400 is connected with the CT data acquisition device 100, the heart gravity center extraction device 200 and the vertebra gravity center extraction device 300 and is used for removing lung tissues, descending aorta, the vertebra, the ribs, the left atrium and the left ventricle from the CT three-dimensional image to obtain a non-interference coronary artery tree image; a coronary artery tree extraction device 500 connected with the non-interference coronary artery tree image acquisition device 400 and used for extracting a coronary artery tree from the non-interference coronary artery tree image; the coronary entry point extraction means 600 is connected to the coronary tree extraction means 500 for extracting a coronary entry point from the coronary tree.

The present application provides a computer storage medium, a computer program being executed by a processor for implementing the above-mentioned method for acquiring a coronary tree and a coronary access point based on a CT sequence image.

As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system. Furthermore, in some embodiments, aspects of the invention may also be embodied in the form of a computer program product in one or more computer-readable media having computer-readable program code embodied therein. Implementation of the method and/or system of embodiments of the present invention may involve performing or completing selected tasks manually, automatically, or a combination thereof.

For example, hardware for performing selected tasks according to embodiments of the invention could be implemented as a chip or a circuit. As software, selected tasks according to embodiments of the invention could be implemented as a plurality of software instructions being executed by a computer using any suitable operating system. In an exemplary embodiment of the invention, one or more tasks according to exemplary embodiments of the methods and/or systems as described herein are performed by a data processor, such as a computing platform for executing a plurality of instructions. Optionally, the data processor comprises volatile storage for storing instructions and/or data and/or non-volatile storage for storing instructions and/or data, e.g. a magnetic hard disk and/or a removable medium. Optionally, a network connection is also provided. A display and/or a user input device, such as a keyboard or mouse, is optionally also provided.

Any combination of one or more computer readable media may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following:

an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

For example, computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer (e.g., a coronary artery analysis system) or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

The above embodiments of the present invention have been described in further detail for the purpose of illustrating the invention, and it should be understood that the above embodiments are only illustrative of the present invention and are not to be construed as limiting the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A method for acquiring a coronary tree and a coronary access point based on CT sequence images, comprising:

acquiring three-dimensional data of a CT sequence image;

2. The method of claim 1 for acquiring a coronary tree and a coronary access point based on CT sequence images, wherein the method of acquiring a center of gravity of a heart from the three-dimensional data comprises:

drawing a gray level histogram of the CT image;

if V is b, picking up a starting point corresponding to the gray value region, and projecting the starting point onto the CT three-dimensional imageAcquiring a three-dimensional image of a heart region, and picking up the physical gravity center of the three-dimensional image of the heart region, namely the heart gravity center P₂；

Wherein b represents a constant, 0.2 < b < 1.

3. The method of claim 2, wherein the method of obtaining the center of gravity of the spine from the three-dimensional data comprises:

Wherein a represents a constant, 0 < a < 0.2.

4. The method for acquiring coronary tree and coronary access points based on CT sequence images as claimed in claim 3, wherein the method for removing lung tissue from the CT three-dimensional images comprises:

If the gray value in the gray histogram is less than Q_{Lung (lung)}And removing the image corresponding to the gray value to obtain a first image with the lung tissue removed.

5. The method of claim 4 for coronary tree and coronary access point acquisition based on CT sequence images, wherein the method of removing descending aorta from the CT three-dimensional images comprises:

according to the descending aorta and the heart center O₁And the spine and the heart circleHeart O₁Obtaining a circle corresponding to the descending aorta;

the descending aorta is removed from the first image resulting in a second image.

6. The method of claim 5, wherein said setting of said descending aorta gray scale threshold Q is based on a method of acquiring coronary trees and coronary access points from CT sequence images_DescendThe method for performing binarization processing on the first image comprises the following steps:

acquiring the gray value in the first image larger than the descending aorta gray threshold Q_DescendCalculating the average gray value of the pixel point PO

according to

Carrying out binarization processing on the first image, removing impurity points in the first image to obtain a binarized image, wherein k is a positive integer and Q is_kThe gray value corresponding to the kth pixel point PO is represented, and p (k) represents the pixel value corresponding to the kth pixel point PO.

7. Method for acquiring coronary trees and coronary entry points based on CT sequence images, as claimed in claim 5, characterized in that said method is based on the descending aorta and the heart center O₁And the center O of the spine and the heart₁The method for obtaining the circle corresponding to the descending aorta comprises the following steps:

8. The method of claim 7, wherein the method of obtaining the approximate region of the spine and the approximate region of the descending aorta comprises:

9. The method of claim 8, wherein the method of obtaining the descending aorta image, i.e. the circle corresponding to the descending aorta, by removing error pixels according to the approximate region of the descending aorta comprises:

10. The method of claim 9, wherein the method of removing descending aorta from the first image and obtaining the second image comprises:

if the number of circle centers in the seed point list is more than or equal to3, calculating the average radius of all the seed points

And the mean center point P₃；

Calculate by P₃As a center of circle, in

Setting a parameter a to obtain a connected domain gray threshold

Wherein a is a positive number;

recalculating the center point P of the connected component₄；

if P is on said two-dimensional slice of the b-th layer₃And P₄If the Euclidean distance is less than or equal to m, extracting pixel points with the gray value greater than 0 in the two-dimensional slice of the b-th layer, and adding P on the two-dimensional slice of the b-th layer₃Point set to P₄Point; and setting the pixel value corresponding to the pixel point PO of all the two-dimensional slices on the (b + 1) th layer and above as 0 to obtain the images corresponding to the first layer to the (b) th layer, which are the second images.

11. The method for acquiring coronary tree and coronary access point based on CT sequence images according to claim 10, wherein the method for removing vertebrae according to the CT three-dimensional images comprises:

12. The method of claim 11, wherein the method of removing ribs from the CT three-dimensional image comprises:

13. The method for acquiring a coronary tree and a coronary access point based on CT sequence images as claimed in claim 11, wherein the method for removing the left atrium and left ventricle from the CT three-dimensional images comprises:

extracting an aorta centerline from the fifth image;

obtaining a connected domain image of the left atrium and left ventricle;

14. The method of claim 11, wherein the method of extracting the aorta centerline from the fifth image comprises:

filtering circle center P_5kGenerating a new point list;

radius of filtration R_kGenerating a new radius list;

15. The method of claim 14, wherein the step of hierarchically slicing the fifth image to obtain a binarized set of images comprises:

B) setting a coronary Tree Gray threshold Q_{Crown 1}(ii) a According to

16. CT sequence based image acquisition according to claim 14Method for coronary tree and coronary entry point, characterized in that said circle center P is obtained from each layer of said slices in said binarized image set_5kAnd the corresponding circle has a radius R_kThe method comprises the following steps:

E) if N is present_k≤N_{Threshold 1}，R_k＝R_{Threshold 1}M, wherein N_kRepresenting the number of pixel points in the point list of the k-th slice, detecting 1 circle in the k-th slice, and taking the center of the circle as the center O of the circle_kStep I is carried out, and if no circle is detected, step H is carried out;

F) if N is present_k≤N_{Threshold 1}，R_k≠R_{Threshold 1}Detecting 3 circles in the k layer slice, if 3 circles are detected, performing step I, and if 3 circles are not detected, performing step H;

G) if N is present_k＞N_{Threshold 1}If the circle center is determined again, the point with the circle center in the k-1 layer slice and the closest distance to the tail point D in the point list is taken as the circle center O_kStep I is carried out, and if no circle is detected, step H is carried out;

H) detecting N_kAnd N_{Threshold 1}-1, repeating said steps E to G, if no circle has been detected yet, detecting N and N_{Threshold 1}-2, repeating said steps E to G; and so on until finding the center of a circle O_k；

I) With the center of circle O_kAs starting points, positive direction along X axis, negative direction and positive direction along Y axisFinding 3 points with the gray value of 0 in the direction; determining a circle according to the 3 points to find the circle center P_5kAnd a radius R_k。

17. The method for acquiring coronary tree and coronary entry points based on CT sequence images as claimed in claim 16, wherein said filtering circle center P_5kThe method for generating the new point list comprises the following steps:

K) If the center of the circle P_5kIf the gray value on the fifth image is less than 0, the process from the step E to the step I is repeated until the radius R is found₁≥R_{Threshold 2}And the gray value is greater than or equal to the circle center P of 0_5k；

18. The method for acquiring coronary tree and coronary entry points based on CT sequence images as claimed in claim 17, wherein the radius R is filtered_kThe method for generating the new radius list comprises the following steps:

N) if N_k≥N_{Threshold 2}Or N is_k＜N_{Threshold 2}、L≤L_Threshold(s)Will deviate from the center P_5kThe radius value of the far point is replaced by the average radius value of the remaining points as R_kThe radius R is set_kAnd filling the radius list to generate a new radius list.

19. The method of claim 13, wherein the method of obtaining a sixth image along a vertical plane of the centerline of the aorta from the trend of the centerline of the aorta comprises:

20. The method for acquiring a coronary tree and a coronary access point based on CT sequence images as claimed in claim 13, wherein the method for acquiring connected domain images of the left atrium and left ventricle comprises:

if the pixel points in the extension section curve are positioned in the fifth image and the gray value Q of the pixel points in the extension section curve is more than Q_DescendThen extracting the pixel point to obtain the aorta image, namely the firstSeven images;

subtracting the seventh image from the fifth image to obtain an eighth image;

21. The method of claim 13, wherein the method of extracting coronary trees and coronary access points from the non-interfering coronary tree image comprises:

acquiring a coronary artery tree image according to the eleventh image;

extracting a coronary entry point from the coronary tree image.

22. The method of claim 21, wherein the step of obtaining the connected component image of the coronary tree from the tenth image is an eleventh image, and the step of obtaining the coronary tree image according to the eleventh image comprises:

23. The method of claim 22, wherein the method of extracting coronary tree and coronary access points from the aorta image comprises:

24. A computer storage medium, wherein a computer program is executed by a processor to implement the method of acquiring a coronary tree and a coronary entry point based on CT sequence images as claimed in any one of claims 1 to 23.

25. A system for a method of acquiring a coronary tree and coronary entry points based on CT sequence images as claimed in any one of claims 1 to 23, comprising: a CT data acquisition device, a heart gravity center extraction device, a vertebra gravity center extraction device, an interference-free coronary artery tree image acquisition device, a coronary artery tree extraction device and a coronary artery entry point extraction device;