CN107527341B - Method and system for processing angiography image - Google Patents
Method and system for processing angiography image Download PDFInfo
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Abstract
The invention relates to a processing method of an angiography image, which comprises the following steps: in an angiographic image, determining a target region containing coronary arteries; performing linear enhancement on the target region containing the coronary artery; determining a preset gray threshold range of coronary arteries; storing voxel points of which the gray values are within a preset gray threshold range; the voxel points are processed to form a coronary image so that the coronary image can be easily stripped from the heart chamber image.
Description
Technical Field
The invention belongs to the technical field of image recognition and processing, and particularly relates to an image processing method and system for blood vessels.
Background
The heart is a key organ of the cardiovascular system of the human body. Modern medical imaging techniques are capable of providing structural and functional information of the heart. Among them, CT (Computed Tomography) and MR (Magnetic Resonance) have fast imaging speed, high resolution and large information amount, and are important means for cardiac examination. The angiography can assist in positioning pathological change positions such as coronary heart disease and angina caused by coronary artery pathological change and evaluating pathological change degree. The coronary artery includes two parts, a left coronary artery and a right coronary artery. The left coronary artery and the right coronary artery respectively originate from the aorta at the bottom of the heart, extend in the direction of the apex of the heart, and envelope the surface of the heart. The farther from the root of the coronary artery, the thinner the coronary artery.
In implementing the conventional technique, the inventors found that the following technical problems exist:
the key step of the cardiac angiography technology is to locate and extract the left and right coronary vessels from the angiography image. Although contrast agent is present in coronary artery, contrast agent is present in part of the chamber of the heart, and the coronary artery is on the surface of the chamber, so in angiography, the coronary artery image and the heart chamber image are stripped, and the operation difficulty is high.
Disclosure of Invention
Therefore, it is necessary to provide a method for processing coronary artery images in angiography, which is directed to the technical problems that the coronary artery has contrast agent, the contrast agent also exists in a partial chamber of the heart, and the coronary artery has great operation difficulty in stripping the coronary artery images from the heart chamber images due to the fact that the coronary artery has the surface of the partial chamber of the heart.
According to a first aspect of the present application, a method for processing an angiographic image is proposed, comprising the steps of:
in an angiographic image, determining a target region containing coronary arteries;
performing linear enhancement on the target region containing the coronary artery;
determining a preset gray threshold range of coronary arteries;
storing voxel points of which the gray values are within a preset gray threshold range;
and processing the voxel points to form a coronary artery image.
In one embodiment, the step of determining the target region of the coronary artery comprises:
in the angiography image, an aorta image and a pericardium image distributed with coronary arteries are obtained through segmentation;
and expanding the pericardium image to the periphery by a preset size to form a determined target region of the coronary artery.
In one embodiment, the step of determining the target region of the coronary artery comprises:
in the angiography image, segmenting to obtain an aorta image;
from the aorta image, a target region of coronary arteries is determined.
In one embodiment, the linear enhancement of the target region including coronary arteries includes:
and performing linear enhancement on the angiography image by adopting multi-scale filtering or an enhancement algorithm based on a Hessian matrix.
In one embodiment, the step of determining the preset gray threshold range of the coronary artery comprises:
acquiring a voxel volume accumulated image with a gray value corresponding to the gray value;
determining a coronary voxel volume value;
and determining a preset gray threshold range of the coronary artery according to the gray value-gray value corresponding voxel volume accumulated image by taking the preset multiple coronary artery voxel volume value as the gray value corresponding voxel volume accumulated value.
In one embodiment, the step of processing the pixel points to form a coronary artery image includes:
according to a set gray threshold, carrying out first screening on the pixel points to obtain a first subset;
determining the starting point and the gravity center of each connected domain in the first subset;
determining the corresponding connected domains of the left coronary artery and the right coronary artery according to the starting point and the gravity center of each connected domain;
and extracting the initial connected domain of the left coronary artery and the initial connected domain of the right coronary artery in the corresponding connected domains of the left coronary artery and the right coronary artery respectively.
In one embodiment, the method further comprises:
determining a position of an aortic image end layer in the angiographic image;
and performing secondary screening on the connected regions corresponding to the left coronary artery and the right coronary artery according to the position of the aorta image tail end layer.
According to a second aspect of the present application, an angiographic image processing method is proposed, comprising the steps of:
acquiring an angiographic image;
in an angiographic image, determining a target region containing a target blood vessel;
performing linear enhancement on the target region containing the target blood vessel;
determining a preset gray threshold range corresponding to a target blood vessel;
storing voxel points of which the gray values are within a preset gray threshold range;
and processing the voxel points to form a target blood vessel image.
In one embodiment, the target vessel is at least one of a coronary artery, a retinal vessel, or a hepatic vessel in the cardiac region.
According to a third aspect of the present application, an angiographic image processing system is proposed, comprising:
one or more processors;
storage means for storing one or more programs for performing the operations of:
acquiring an angiographic image;
in an angiographic image, determining a target region containing a target blood vessel;
performing linear enhancement on the target region containing the target blood vessel;
determining a preset gray threshold range corresponding to a target blood vessel;
storing voxel points of which the gray values are within a preset gray threshold range;
and processing the voxel points to form a target blood vessel image.
Drawings
Fig. 1 is a flowchart of a processing method for providing a coronary artery image in angiography according to an embodiment of the present application.
Fig. 2 is a diagram for providing an isolated aorta image and a pericardium image with coronary arteries distributed after threshold segmentation in angiography according to an embodiment of the present application.
Fig. 3 is an image obtained by performing linear enhancement on an angiographic image with a preset voxel size according to an embodiment of the present disclosure.
Fig. 4 is an image for determining preset gray threshold values of left and right coronary images according to a voxel volume accumulation value corresponding to a gray value corresponding to a 4-fold coronary voxel volume value.
FIG. 5 is an image of a first subset of a set of connected components formed by removing connected components having a number of voxel points in the set of connected components that does not exceed a predetermined first number.
Fig. 6 is an image of connected component elements located above the distal aortic layer, within a predetermined second voxel size from the aorta image, with the starting point remaining.
Fig. 7 is an image of 5 longest connected component elements selected from the left and right coronary connected component candidate elements.
Fig. 8 is an image of determining the left and right coronary artery start connected domain.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. 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 application.
To facilitate an understanding of the technology of the present application, the technology referred to in this application is briefly described as follows:
computed Tomography (CT) is an examination of image diagnostics. X-Ray Computed Tomography (X-CT) is a three-dimensional radiographic medical image reconstructed using digital geometric processing. The X-ray computed tomography mainly irradiates a human body through the rotation of X-rays of a single axial plane, and because different tissues have different absorption capacities to the X-rays, a fault plane image can be reconstructed by using a three-dimensional technology of a computer. Then, a tomographic image of the corresponding tissue can be obtained through Window width (Windowing) or Window level (Window) processing. And finally, stacking the tomograms layer by layer to form a three-dimensional image.
Data for an X-ray slice is acquired by the source of the X-rays around the subject. The sensor is placed diagonally to the X-ray source. The acquired data are also continuously processed during the pushing of the subject inside the source, and finally the image is obtained through a series of numerical operations, so-called tomographic reconstruction.
Different radiation intensities (radiationness) correspond to 256 different degrees of gray scale values, and the gray scale values of 0-255 are displayed through an 8-bit display. Specifically, the Dynamic Range (DR) between the minimum value and the maximum value of the data is converted to 256 gray-scale values of 0 to 255 represented by 8 bits.
Window Technique (Window Technique) is a display Technique for observing normal tissues or lesions of different densities, and includes Window Width and Window Level.
The window width is a process of calculating an image from data obtained by a Hounsfield Unit (HU). The window width is the range of computed tomography dynamic (CT/DR) values displayed on the CT/DR image. Tissues and lesions are shown in different simulated gray levels within the range of CT/DR values. For the determined CT/DR value range, tissues and lesions higher than the range are all displayed in white shadow no matter how much the tissues and lesions are higher than the range, and the gray level difference is avoided; on the contrary, tissues and lesions below this range, no matter how much, are shown in black shadow, and there is no difference in degree. The window width is increased, the CT/DR value range shown by the image is enlarged, the tissue structures with different radiation intensities are displayed, but the gray level difference among the structures is reduced; decreasing the window width results in a decrease in the display texture and an increase in the gray scale difference between the structures.
The window level is the center position of the window. The same window width and different window levels include the difference of CT/DR values in the CT/DR range. For example, the window width is W ═ 60HU, and when the window level is L ═ 0HU, the CT/DR value ranges from-30 HU to +30 HU; and when the window level L is 10HU, the CT/DR value ranges from-20 HU to +40 HU. Generally, to observe the structure and the occurrence of pathological changes of a certain tissue, the CT/DR value of the tissue should be used as the window level to obtain the best effect. Assuming that the Window level is unchanged, after the defined Window width is narrowed, we refer to as Narrow Window level (Narrow Window), small changes in detail can be resolved, which is called contrast compression.
The three-dimensional reconstruction refers to reconstructing a three-dimensional image of an organ by using a signal, namely attenuation of X-rays, measured by a sensor of a tomography imaging instrument through a human body by a mathematical method. There are two main types of reconstruction methods currently in use: filtered Backprojection (Filtered Backprojection) and convolutional Backprojection (Convolution Backprojection).
Currently, X-CT imaging is isotropic (the resolution of X, y, z axes is the same) or close to isotropic, so that with the aid of computer three-dimensional reconstruction techniques, images can be viewed from different viewpoints by stacking all small voxels.
The threshold of the radiation intensity (radioactivity) can be adjusted, and when the threshold is fixed, an edge detection image processing method can be used to image the three-dimensional object. Different targets may be imaged with different thresholds and different colors used to represent different anatomical structures, such as bone, muscle and cartilage, however, the further fine structures of the structures cannot be imaged.
In voxel imaging (Volume Rendering), more detail can be presented with the feature that transparency and color can be displayed in a single image. For example: the pelvis is visualized in a semi-transparent manner, even at oblique angles, with small portions of other anatomical imaging not obstructing other important portions.
For some parts, although the structures are different, the structures have similar radiation resistance, and the parts cannot be distinguished by simply changing the parameters of voxel imaging. Unwanted portions can be removed manually or automatically, a method commonly referred to as Segmentation.
CT angiography is a technique of CT in which a contrast agent is introduced to reduce the permeability of blood to X-rays, so that blood vessels are displayed as high-transmission density images on a CT sheet, thereby distinguishing the blood vessels from other tissues. Generally, to facilitate the observation of lesions, image reconstruction is performed by a computer to display images on different sections.
The following description is provided to explain embodiments of the present application in order to facilitate understanding of the technical content of the present application.
The application provides an angiographic image processing method, which comprises the following steps: in an angiographic image, determining a target region containing a target blood vessel; performing linear enhancement on the angiography image in the determined target region containing the target blood vessel; determining a preset gray threshold range corresponding to a target blood vessel; storing voxel points of which the gray values are within a preset gray threshold range; processing the voxel points forms a target blood vessel image. Optionally, the target vessel is a coronary artery, a retinal vessel, a liver vessel, or the like in the cardiac region. Referring to fig. 1, a method for processing a coronary artery image in angiography provided by the present application includes the following steps:
s100: in an angiographic image, a target region of a coronary artery is determined.
In the application, the areas of the pericardium and the aorta distribution can be determined manually or automatically in an angiography image by adopting an image segmentation technology, and the target area of the coronary artery can be further determined according to the areas of the pericardium and the aorta distribution.
Further, in another embodiment provided by the present application, the step of determining the target region of the coronary artery includes:
in the angiography image, an aorta image and a pericardium image distributed with coronary arteries are obtained through segmentation;
and expanding the pericardium image to the periphery by a preset size to form a determined target region of the coronary artery. It should be noted that the pericardial image in the present application may also be referred to as a heart boundary, a heart contour, or a heart region.
In one embodiment, the pericardial image is obtained by: acquiring a medical image sequence corresponding to an angiogram image, wherein the medical image sequence comprises a part corresponding to a heart region and is a three-dimensional image formed by a plurality of 2D slices/slices; determining a reference layer image in a medical image sequence and performing cardiac segmentation on the reference layer image; determining a narrow-band region containing a heart boundary according to a heart segmentation result of the reference layer image; and applying a graph cut algorithm narrow-band graph cut algorithm in the narrow-band region to segment the heart of the medical image sequence layer by layer. The angiographic image of the present application may be a magnetic resonance angiographic image or a CT angiographic image or an angiographic image of a PET-CT image, an angiographic image of a PET-MR image, or the like. In the following description, a CT angiography image is taken as an example.
Optionally, applying a segmentation algorithm to the narrowband region to segment the heart of the CT angiography image layer by layer, including: taking an image layer adjacent to the reference layer image as a current layer image; extracting a boundary of the current layer image as a first edge image based on the gradient; determining an optimal closed curve for an image area corresponding to the narrow-band area in the first edge image through a graph cut algorithm, and then performing heart segmentation on the current layer image according to the optimal closed curve; and taking the current layer image as a previous layer image, taking an image which is adjacent to the current layer image and is not subjected to heart segmentation as the current layer image, and returning to continue to perform heart segmentation on the current layer image until the heart region is segmented.
In another embodiment, the pericardial image is obtained by: acquiring a medical image sequence corresponding to a CT (computed tomography) angiogram, wherein the medical image sequence comprises a part corresponding to a heart region and is a three-dimensional image formed by a plurality of 2D slices/slices; respectively determining a starting layer and an ending layer along the Z-axis direction (vertical cross section direction) in the medical image sequence, and determining a reference layer image according to the starting layer and the ending layer, wherein the reference layer image is between the starting layer and the ending layer; segmenting in the reference layer to obtain a heart segmentation result; detecting the position of the liver and the stomach which appear on the cross section in the CT angiography image according to the segmentation result of the heart of the reference layer; because the liver and the stomach are in adjacent position with the heart, the segmentation result of the heart can be assisted according to the position of the liver and the stomach, which appear on the cross section. In this embodiment, the modified stop layer of heart segmentation is determined by using the detected position where the stomach begins to appear on the cross section, and the heart is divided into an upper segment and a lower segment by using the detected position where the liver begins to appear on the cross section, and because the upper segment of the heart is only connected with the lung, the heart boundary is obvious, and the heart can be segmented by adopting a simpler gradient detection method; the lower heart is connected with multiple organs of the liver, the stomach and the lung, the heart boundary is fuzzy, and a method which is more complicated than that of the upper section is needed to detect the heart boundary.
Referring to fig. 2, a separated aorta image and a pericardium image with coronary artery distribution are formed after the threshold segmentation provided by the present application. Fig. 2 shows, from various perspectives, separate images of the aorta and the pericardium with the coronary arteries distributed. The pericardial image is expanded by a preset size around, for example, a preset voxel size around, for example, 7 voxels in front, back, left, and right, and 5 voxels in up and down. Thus, a target region of the determined coronary artery can be formed.
Further, in another embodiment provided by the present application, the step of determining the target region of the coronary artery includes:
in the CT angiography image, segmenting to obtain an aorta image;
from the aorta image, a target region of coronary arteries is determined. The extraction or identification process of the aorta image can be referred to as: aorta segmentation method in CT images [ J ] modern scientific instruments 2013(2): 45-48; or an interactive three-dimensional segmentation method [ J ] based on a watershed algorithm, Chinese tissue engineering research 2011,15(39):7351-7354 and the like.
S200: the target region of the coronary artery is linearly enhanced.
The linear enhancement method can adopt multi-scale filtering or an enhancement algorithm based on a Hessian matrix. Among these, the enhancement algorithms of multi-scale filtering can be referred to in the document Multiscale Vessel enhancement filtering [ C ]// International Conference on Medical Image Computing and Computer-assisted amplification. Springer Berlin Heidelberg,1998:130-137 or Vessel enhancement filter using direct filter bank [ J ]. Computer Vision and ImageUnderstand, 2009,113(1): 101-112. The enhancement algorithm based on the Hessian matrix can be referred to in the literature Selective enhancement filters for nodules, vessels, and air waves in two and two dimensional CT scans [ J ]. Medical physics,2003,30(8): 2040-2051.
In one embodiment provided by the present application, the step of linearly enhancing the CT angiography image includes:
performing linear enhancement of the CT angiographic image by a preset voxel size to highlight coronary arteries:
it is assumed that a linear target region (a blood vessel in this embodiment) in a CT angiography image may be subjected to gaussian filtering;
then calculating the eigenvalue of the three-dimensional Hessian matrix, wherein the eigenvalue uses lambda1、λ2And λ3Is expressed, and the three eigenvalues correspond to the second reciprocal fxx、fyy、fzz. Considering the characteristic of the point presented at the bifurcation of the coronary artery, we use the characteristics of the point and the line to satisfy the following formula:
Kline(λ1,λ2,λ3)＝(|λ2|-|λ3|)/|λ1|,Kdot＝|λ3|/|λ1for example, |), the formula we use for simultaneous enhancement of points and lines is as follows:
Kline&dot＝λ2。
further, in another embodiment provided herein, the preset voxel size is 1-3 voxels.
The display effect after linear enhancement is shown in fig. 3.
S300: a preset gray threshold range for the coronary arteries is determined.
It will be appreciated that the determination of the preset gray level threshold range for the coronary arteries is here an operation of determining the window width and level in the previously described window technique.
Further, in another embodiment provided by the present application, the step of determining the preset gray threshold range of the coronary artery includes:
acquiring a voxel volume accumulated image corresponding to a gray value-gray value;
determining a coronary voxel volume value;
and determining a preset gray threshold range of the coronary artery according to the gray value-gray value corresponding voxel volume accumulated image by taking the preset multiple coronary artery voxel volume value as the gray value corresponding voxel volume accumulated value.
And making a histogram of a voxel volume accumulation image corresponding to gray value-gray value on the image obtained by performing linear enhancement on the CT angiography image.
By knowing the stroke volume of a human body and statistically analyzing the existing coronary data, about 3000 voxels of volume of the left and right coronary are known.
Due to the linear enhancement, the voxel volume accumulation with the preset multiple of 3-5 times comprises the left coronary image and the right coronary image, and more preferably, the voxel volume accumulation with the preset multiple of 4 times comprises the left coronary image and the right coronary image.
Therefore, the preset gray threshold range of the coronary artery can be determined according to the voxel volume accumulation value corresponding to the gray value and the voxel volume accumulation image corresponding to the gray value-gray value according to the preset multiple of the coronary artery voxel volume value. As shown in fig. 4, the preset gray level threshold of the left and right coronary images is determined to be 143HU according to the voxel volume accumulation value corresponding to the 4-fold coronary artery voxel volume value as the gray level value.
S400: and storing the voxel points of which the gray values are within the preset gray threshold range.
And storing the voxel points with the gray values larger than the preset gray threshold value. The preset grayscale threshold may be a first grayscale threshold, such as 143U. It should be noted that, for a three-dimensional medical image, a voxel point in the present application may also be referred to as a pixel point.
S500: and processing the voxel points to form a coronary artery image.
Further, in another embodiment provided by the present application, the step of processing the voxel points to form a coronary artery image includes: 1) and according to a set gray threshold, carrying out first screening on the pixel points to obtain a first subset. In this embodiment, setting the grayscale threshold may select a second set grayscale threshold, the process including: firstly, determining a connected domain set formed by continuous voxel points; then, connected domain elements with the number of the voxel points not exceeding a preset first number in the connected domain set are removed to form a first subset of the connected domain set.
2) The start and center of gravity of each connected component in the first subset is determined. Optionally, a point in the first subset at which each connected component is closest to the aorta image is determined as a starting point of the connected component.
3) And determining the corresponding connected domains of the left coronary artery and the right coronary artery according to the starting point and the gravity center of each connected domain. Optionally, it is determined whether each connected component in the first subset belongs to the left coronary artery connected component set or the right coronary artery connected component set according to the position of the starting point and the gravity center of the connected component.
4) And extracting the initial connected domain of the left coronary artery and the initial connected domain of the right coronary artery in the corresponding connected domains of the left coronary artery and the right coronary artery respectively. Optionally: leaving a position from the starting point in a set of left coronary artery connected domains within a preset second voxel size, forming a second subset of the set of connected domains;
in the second subset, a second number of connected component elements are taken as candidate elements of the left coronary connected component in a sequence from long to short according to length;
determining a connected component which is closest to the aorta image in the candidate components of the left coronary artery connected component as an initial connected component of the left coronary artery;
leaving a position from the starting point in a set of right coronary artery connected domains within a preset second voxel size, forming a third subset of the set of connected domains;
in the third subset, a second number of connected component elements are taken as candidate elements of the right coronary artery connected component before the connected component elements are sorted from long to short according to length;
and determining a connected component which is closest to the aorta image in the candidate elements of the right coronary artery connected component as an initial connected component of the right coronary artery.
Optionally, in order to exclude the influence of other connected domains in coronary extraction, the following operations may also be performed: determining a position of an aortic image end layer in the angiographic image; and performing secondary screening on connected domains corresponding to the left coronary artery and the right coronary artery according to the position of the aortic image terminal layer, wherein the screening step can comprise the following steps: only connected domain elements located above the aortic end layer remain.
Specifically, a connected domain set formed by continuous voxel points is determined. Then, connected domain elements, the number of which does not exceed the preset first number, included in the connected domain set are removed to form a first subset of the connected domain set, as shown in fig. 5. The predetermined first number is 20-50, wherein the predetermined first number is 35 is most preferred. And determining the point of each connected component in the first subset, which is closest to the aorta image, as the starting point of the connected component. And determining whether each connected domain element in the first subset belongs to the left coronary artery connected domain set or the right coronary artery connected domain set according to the position of the starting point and the gravity center of the connected domain element. The position of the reserved starting point in the left coronary artery connected domain set is within the preset second voxel size from the aorta image and is positioned above the aorta end layer, and a second subset of the connected domain set is formed. The position where the starting point is retained in the right coronary artery connected component set is within the preset second voxel size from the aorta image and is located above the aorta distal layer, forming a third subset of the connected component set, as shown in fig. 6. And in the second subset, a second number of connected component elements are taken as candidate elements of the left coronary connected component in a sequence from long to short according to the length. And in the third subset, a second number of connected component elements are taken as candidate elements of the right coronary connected component in a sequence from long to short according to the length. The predetermined second number is 3-7, wherein the predetermined second number is optimal when the predetermined second number is 5, as shown in fig. 7. And determining a connected component which is closest to the aorta image in the candidate components of the left coronary artery connected component as an initial connected component of the left coronary artery. And determining the connected component closest to the aorta image in the candidate components of the right coronary artery connected component as the initial connected component of the right coronary artery, as shown in fig. 8.
Because of the linear enhancement of the CT angiographic image, the coronary images can be easily stripped from the heart chamber images.
The present application also provides an angiographic image processing method, comprising the steps of:
acquiring an angiographic image;
in an angiographic image, determining a target region containing a target blood vessel;
performing linear enhancement on a target region containing a target blood vessel;
determining a preset gray threshold range corresponding to a target blood vessel;
storing voxel points of which the gray values are within a preset gray threshold range;
and processing the voxel points to form a target blood vessel image.
Further, in yet another embodiment provided herein, the target vessel is at least one of a coronary artery, a retinal vessel, or a hepatic vessel in the cardiac region.
The present application further provides a CT angiography image processing system, comprising:
one or more processors;
storage means for storing one or more programs for performing the operations of:
acquiring an angiographic image, which may be an MR angiographic image or a CT angiographic image;
in an angiographic image, determining a target region or a region of interest containing a target blood vessel;
performing linear enhancement on the angiography image in the determined region of interest containing the target blood vessel;
determining a preset gray threshold range corresponding to a target blood vessel;
storing voxel points of which the gray values are within a preset gray threshold range;
and processing the voxel points to form a target blood vessel image.
A specific application scenario of the present application is introduced as follows:
acquiring a medical image sequence corresponding to a CT (computed tomography) angiogram, wherein the medical image sequence comprises a part corresponding to a heart region and is a three-dimensional image formed by a plurality of 2D slices/slices; determining a reference layer image in a medical image sequence and performing cardiac segmentation on the reference layer image; determining a narrow-band region containing a heart boundary according to a heart segmentation result of the reference layer image; and applying a graph cut algorithm narrow-band graph cut algorithm in the narrow-band region to segment the heart of the medical image sequence layer by layer.
Applying a segmentation algorithm in a narrow band region to segment the heart of the CT angiography image layer by layer, wherein the segmentation algorithm comprises the following steps: taking an image layer adjacent to the reference layer image as a current layer image; extracting a boundary of the current layer image as a first edge image based on the gradient; determining an optimal closed curve for an image area corresponding to the narrow-band area in the first edge image through a graph cut algorithm, and then performing heart segmentation on the current layer image according to the optimal closed curve; and taking the current layer image as a previous layer image, taking an image which is adjacent to the current layer image and is not subjected to heart segmentation as the current layer image, and returning to continue to perform heart segmentation on the current layer image until the heart region is segmented.
Or acquiring a medical image sequence corresponding to the CT angiography image, wherein the medical image sequence comprises a part corresponding to a heart region and is a three-dimensional image formed by a plurality of 2D slices/slices; respectively determining a starting layer and an ending layer along the Z-axis direction (vertical cross section direction) in the medical image sequence, and determining a reference layer image according to the starting layer and the ending layer, wherein the reference layer image is between the starting layer and the ending layer; segmenting in the reference layer to obtain a heart segmentation result; detecting the position of the liver and the stomach which appear on the cross section in the CT angiography image according to the segmentation result of the heart of the reference layer; because the liver and the stomach are in adjacent position with the heart, the segmentation result of the heart can be assisted according to the position of the liver and the stomach, which appear on the cross section. In this embodiment, the modified stop layer of heart segmentation is determined by using the detected position where the stomach begins to appear on the cross section, and the heart is divided into an upper segment and a lower segment by using the detected position where the liver begins to appear on the cross section, and because the upper segment of the heart is only connected with the lung, the heart boundary is obvious, and the heart can be segmented by adopting a simpler gradient detection method; the lower heart is connected with multiple organs of the liver, the stomach and the lung, the heart boundary is fuzzy, and a method which is more complicated than that of the upper section is needed to detect the heart boundary.
After heart segmentation, a separate aorta image and a pericardium image with coronary arteries distributed are formed.
The pericardial image is expanded by a preset size around, for example, a preset voxel size around, for example, 7 voxels in front, back, left, and right, and 5 voxels in up and down. Thus, a target region of the determined coronary artery can be formed.
And performing linear enhancement on the CT angiography image by adopting multi-scale filtering or an enhancement algorithm based on a Hessian matrix.
Then, acquiring a voxel volume accumulated image corresponding to the gray value-gray value; determining a coronary voxel volume value; and determining a preset gray threshold range of the coronary artery according to the gray value-gray value corresponding voxel volume accumulated image by taking the preset multiple coronary artery voxel volume value as the gray value corresponding voxel volume accumulated value.
And storing the voxel points with the gray values larger than the preset gray threshold value.
Determining a connected domain set formed by the continuous voxel points;
removing connected domain elements of which the number of voxel points does not exceed a preset first number from a connected domain set to form a first subset of the connected domain set;
determining the point of each connected domain element in the first subset, which is closest to the aorta image, as the starting point of the connected domain element;
determining whether each connected domain element in the first subset belongs to a left coronary artery connected domain set or a right coronary artery connected domain set according to the position of the starting point and the gravity center of the connected domain element;
leaving the position of the starting point in the set of left coronary artery connected components within a preset second voxel size from the aorta image, forming a second subset of the set of connected components;
in the second subset, a second number of connected component elements are taken as candidate elements of the left coronary connected component in a sequence from long to short according to length;
determining a connected component which is closest to the aorta image in the candidate components of the left coronary artery connected component as an initial connected component of the left coronary artery;
leaving the starting point in the right coronary artery connected domain set within a preset second voxel size from the aorta image, forming a third subset of the connected domain set;
in the third subset, a second number of connected component elements are taken as candidate elements of the right coronary artery connected component before the connected component elements are sorted from long to short according to length;
and determining a connected component which is closest to the aorta image in the candidate elements of the right coronary artery connected component as an initial connected component of the right coronary artery.
Preferably, only connected domain elements located above the aortic end layer may be retained in the elements of the second subset.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of processing an angiographic image, comprising the steps of:
in an angiographic image, determining a target region containing coronary arteries;
performing linear enhancement on the target region containing the coronary artery;
determining a preset gray threshold range of coronary arteries;
storing voxel points of which the gray values are within a preset gray threshold range;
processing the voxel points to form a coronary image;
the processing the voxel points to form a coronary image, comprising:
acquiring a first subset of the voxel points, and determining a starting point and a gravity center of each connected domain in the first subset;
and determining the connected domains corresponding to the left coronary artery and the right coronary artery respectively according to the starting point and the gravity center of each connected domain, and extracting the starting connected domain of the left coronary artery and the starting connected domain of the right coronary artery respectively in the connected domains corresponding to the left coronary artery and the right coronary artery.
2. The method of claim 1, wherein the step of determining a target region of a coronary artery comprises:
in the angiography image, an aorta image and a pericardium image distributed with coronary arteries are obtained through segmentation;
and expanding the pericardium image to the periphery by a preset size to form a determined target region of the coronary artery.
3. The method of claim 1, wherein the step of determining a target region of a coronary artery comprises:
in the angiography image, segmenting to obtain an aorta image;
from the aorta image, a target region of coronary arteries is determined.
4. The method of claim 1, wherein linearly enhancing the target region including coronary arteries comprises:
and performing linear enhancement on the angiography image by adopting multi-scale filtering or an enhancement algorithm based on a Hessian matrix.
5. The method of claim 1, wherein the step of determining a preset gray threshold range for the coronary arteries comprises:
acquiring a voxel volume accumulated image with a gray value corresponding to the gray value;
determining a coronary voxel volume value;
and determining a preset gray threshold range of the coronary artery according to the gray value-gray value corresponding voxel volume accumulated image by taking the preset multiple coronary artery voxel volume value as the gray value corresponding voxel volume accumulated value.
6. The method of claim 5, wherein the step of processing the voxel points to form an image of a coronary artery comprises:
according to a set gray threshold value, screening the voxel points for the first time to obtain a first subset;
determining the starting point and the gravity center of each connected domain in the first subset;
determining the corresponding connected domains of the left coronary artery and the right coronary artery according to the starting point and the gravity center of each connected domain;
and extracting the initial connected domain of the left coronary artery and the initial connected domain of the right coronary artery in the corresponding connected domains of the left coronary artery and the right coronary artery respectively.
7. The method of claim 6, further comprising:
determining a position of an aortic image end layer in the angiographic image;
and performing secondary screening on the connected regions corresponding to the left coronary artery and the right coronary artery according to the position of the aorta image tail end layer.
8. An angiographic image processing method, characterized by comprising the steps of:
acquiring an angiographic image;
in an angiographic image, determining a target region containing a target blood vessel;
performing linear enhancement on the target region containing the target blood vessel;
determining a preset gray threshold range corresponding to a target blood vessel;
storing voxel points of which the gray values are within a preset gray threshold range;
processing the voxel points to form a target vessel image;
processing the voxel points to form a target blood vessel image, comprising:
acquiring a first subset of the voxel points, and determining a starting point and a gravity center of each connected domain in the first subset;
and determining connected domains corresponding to the target blood vessel according to the starting points and the gravity centers of the connected domains, and extracting the starting connected domains of the target blood vessel in the connected domains corresponding to the target blood vessel.
9. The method of claim 8, wherein the target vessel is at least one of a coronary artery, a retinal vessel, or a hepatic vessel in the cardiac region.
10. An angiographic image processing system comprising:
one or more processors;
storage means for storing one or more programs for performing the operations of:
acquiring an angiographic image;
in an angiographic image, determining a target region containing a target blood vessel;
performing linear enhancement on the target region containing the target blood vessel;
determining a preset gray threshold range corresponding to a target blood vessel;
storing voxel points of which the gray values are within a preset gray threshold range;
processing the voxel points to form a target vessel image;
processing the voxel points to form a target blood vessel image, comprising:
acquiring a first subset of the voxel points, and determining a starting point and a gravity center of each connected domain in the first subset;
and determining connected domains corresponding to the target blood vessel according to the starting points and the gravity centers of the connected domains, and extracting the starting connected domains of the target blood vessel in the connected domains corresponding to the target blood vessel.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108805923B (en) * | 2018-06-01 | 2022-04-01 | 东软医疗系统股份有限公司 | Physiological parameter acquisition method and device |
| CN109345546B (en) * | 2018-09-30 | 2019-09-17 | 数坤（北京）网络科技有限公司 | A kind of coronary artery volume data model dividing method and equipment |
| CN109712163B (en) * | 2018-12-05 | 2021-05-18 | 上海联影医疗科技股份有限公司 | Coronary artery extraction method, device, image processing workstation and readable storage medium |
| CN109801277B (en) * | 2019-01-18 | 2021-04-20 | 浙江大学 | Image processing method and device and storage medium |
| CN109949408A (en) * | 2019-03-13 | 2019-06-28 | 安徽紫薇帝星数字科技有限公司 | A kind of medical image method for reconstructing and its system cutting algorithm based on figure |
| CN110264427A (en) * | 2019-06-27 | 2019-09-20 | 四川工商学院 | A kind of coronary angiography method, equipment and computer readable storage medium |
| CN110400626B (en) * | 2019-07-08 | 2023-03-24 | 上海联影智能医疗科技有限公司 | Image detection method, image detection device, computer equipment and storage medium |
| CN110706246B (en) * | 2019-10-15 | 2022-08-02 | 上海微创卜算子医疗科技有限公司 | Blood vessel image segmentation method and device, electronic equipment and storage medium |
| CN111047612B (en) * | 2019-12-25 | 2023-02-24 | 宝鸡市中医医院 | Coronary artery CT angiography image segmentation method |
| CN113506277A (en) * | 2021-07-16 | 2021-10-15 | 推想医疗科技股份有限公司 | Image processing method and device |
| CN113610841B (en) * | 2021-08-26 | 2022-07-08 | 首都医科大学宣武医院 | Blood vessel abnormal image identification method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663709A (en) * | 2012-04-28 | 2012-09-12 | 大连海事大学 | Vessel enhancement method for X-ray angiogram |
| CN104867147A (en) * | 2015-05-21 | 2015-08-26 | 北京工业大学 | SYNTAX automatic scoring method based on coronary angiogram image segmentation |
| CN104978725A (en) * | 2014-04-03 | 2015-10-14 | 上海联影医疗科技有限公司 | Method and device for dividing coronary artery |
| CN106296660A (en) * | 2016-07-28 | 2017-01-04 | 北京师范大学 | A kind of full-automatic coronary artery dividing method |
| CN106447645A (en) * | 2016-04-05 | 2017-02-22 | 天津大学 | Device and method for coronary artery calcification detection and quantification in CTA image |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8311308B2 (en) * | 2009-12-10 | 2012-11-13 | Siemens Corporation | Stent viewing using a learning based classifier in medical imaging |
-
2017
- 2017-08-30 CN CN201710764182.3A patent/CN107527341B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102663709A (en) * | 2012-04-28 | 2012-09-12 | 大连海事大学 | Vessel enhancement method for X-ray angiogram |
| CN104978725A (en) * | 2014-04-03 | 2015-10-14 | 上海联影医疗科技有限公司 | Method and device for dividing coronary artery |
| CN104867147A (en) * | 2015-05-21 | 2015-08-26 | 北京工业大学 | SYNTAX automatic scoring method based on coronary angiogram image segmentation |
| CN106447645A (en) * | 2016-04-05 | 2017-02-22 | 天津大学 | Device and method for coronary artery calcification detection and quantification in CTA image |
| CN106296660A (en) * | 2016-07-28 | 2017-01-04 | 北京师范大学 | A kind of full-automatic coronary artery dividing method |
Non-Patent Citations (3)
| Title |
|---|
| automatic segmentation of coronary arteries using Gabor filters and thresholding based on multiobjective optimization;Ivan Cruz-Aceves 等;《Biomedical Signal Processing and Control》;20160331(第25期);第76-85页 * |
| 冠状动脉立体定量评估关键技术研究;欧阳自鹏;《中国优秀硕士学位论文全文数据库-信息科技辑》;20170315(第3期);第I138-5211页 * |
| 基于局部复杂度信息测度的冠脉造影图像分割;康文炜 等;《光电子.激光》;20110630;第22卷(第6期);第955-960页 * |
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