CN110338830A - The method for automatically extracting neck blood vessel center path in CTA image - Google Patents
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Abstract
The present invention provides a kind of method for automatically extracting neck blood vessel center path in CTA image, this method is applicable in from the arch of aorta and scans to the CTA image data of calvarium, include the following steps: S1: according to blood vessel in the shape, gray scale and position feature in CTA image cross section, the beginning and end of arteria carotis and vertebral artery being automatically positioned respectively；S2: between the blood vessel beginning and end of step S1 positioning, the normalization vessel filter of the multi-scale gradient based on Raycasting is carried out to the region for meeting blood vessel gray feature, constructs vessel filter weight figure；S3: in the vessel filter weight figure of step S2 building, the extraction to arteria carotis and vertebral artery blood vessel center path is completed using Dijkstra optimal path extraction algorithm.Advantages of the present invention: realizing the automatic positioning of blood vessel, participates in manually without user；The center line of extraction is accurate, adjusts without user's later period；The method speed of service is fast, meets real-time demand.
Description
Technical Field
The invention relates to a method for automatically extracting a head and neck blood vessel central path in a CTA image. The invention belongs to the technical field of medical image processing.
Background
Cerebrovascular diseases are one of the main diseases threatening human health at present, and clinically, the three-dimensional structure information of blood vessels is obtained by analyzing images after CTA (non-invasive vascular imaging examination) images and extracting the central path of the blood vessels to assist doctors in judging the degree of vascular lesions. However, since the distribution of blood vessels in a human body is very complex, the blood vessels often pass through bones, and both the bones and the blood vessels present high-brightness gray features in an image generated after the CTA image, which brings great difficulty to the extraction of the central path of the blood vessels and the lesion analysis.
At present, when blood vessel analysis is performed on an image generated after a CTA image, a subtraction technique is usually used to remove bones in the CTA image to obtain a blood vessel region, and then a starting point and a terminating point of a blood vessel are manually defined to obtain a central path of the blood vessel. The disadvantages of this method are: because the subtraction operation requires two scans (CT and CTA scans) of the patient, the patient is likely to shift during the interval between the two scans, which may cause the condition of missing or breaking of the blood vessel structure after subtraction, and further affect the subsequent lesion analysis of the blood vessel.
Although the method for acquiring the central path of the blood vessel by using the blood vessel enhancement method based on Hessian can directly extract the central path of the blood vessel in a CTA image, the method is not ideal in general effect on the blood vessel with complex shape and surrounding tissues, such as the encephalic segment of a vertebra and an internal carotid artery, and also needs a doctor to manually define the starting point and the ending point of the blood vessel, so that certain burden is brought to the analysis work of the doctor.
Disclosure of Invention
In view of the above, the present invention provides a method for automatically extracting central paths of the head and neck blood vessels, i.e., the vertebral artery blood vessels and the carotid artery blood vessels, in the CTA image.
In order to achieve the purpose, the invention adopts the following technical scheme: a method for automatically extracting a central path of a head-neck blood vessel in a CTA image is suitable for CTA image data scanned from an aortic arch to a cranial vertex, and is characterized in that: it comprises the following steps:
s1: automatically positioning the starting point and the end point of the carotid artery and the vertebral artery respectively according to the shape, the gray scale and the position characteristics of the blood vessel in the cross section of the CTA image;
s2: performing Raycasting-based multi-scale gradient normalized blood vessel filtering on the region according with the gray level characteristics of the blood vessel between the start point and the end point of the blood vessel positioned in the step S1 to construct a blood vessel filtering weight value graph;
s3: in the vessel filtering weight map constructed in step S2, the Dijkstra optimal path extraction algorithm is used to complete the extraction of the central paths of the carotid artery and vertebral artery vessels.
The invention has the advantages that: the automatic positioning of the blood vessel is realized without manual participation of a user; the extracted central line is accurate, and the later adjustment of a user is not needed; the method has high running speed and meets the real-time requirement.
Drawings
FIG. 1 is a flow chart of a method for automatically extracting a central pathway of a head and neck blood vessel according to the present invention;
FIG. 2 is a graph of multi-scale gradient normalized vascular filtering based on Raycasting (ray casting);
FIG. 3A is the starting points (green-labeled points) of the carotid and vertebral arteries located after step S1.1.2;
FIG. 3B is the vertebral artery end point (green marker) located after step S1.2.1.2;
FIG. 3C is the carotid endpoint (green marker) located after step S1.2.2.2;
fig. 3D is a graph of the right carotid artery central path extracted after step S3.2 and its corresponding CPR (curved reconstruction) results;
fig. 3E is a graph of the left carotid artery central path extracted after step S3.2 and its corresponding CPR (curved reconstruction) results;
FIG. 3F is a graph of the right vertebral artery central path extracted after step S3.2 and its corresponding CPR (curved reconstruction) results;
fig. 3G is a graph of the left vertebral artery central path extracted after step S3.2 and its corresponding CPR (curved reconstruction) results.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples. It should be noted that various modifications can be made to the embodiments disclosed herein, and therefore, the embodiments disclosed in the specification should not be construed as limiting the present invention, but merely as exemplifications of embodiments thereof, which are intended to make the features of the present invention obvious.
As shown in fig. 1, the method for automatically extracting the central path of the head and neck blood vessels (vertebral artery blood vessels and carotid artery blood vessels) in the CTA image provided by the present invention is suitable for CTA image data (described below as CTA image data meeting the requirements) scanned from the aortic arch to the cranial vertex. The method provided by the invention mainly comprises three key steps:
s1: automatically positioning the starting point and the end point of the carotid artery and the vertebral artery respectively according to the shape, the gray scale and the position characteristics of the blood vessel in the cross section of the CTA image;
s2: performing Raycasting-based multi-scale gradient normalized blood vessel filtering on the region conforming to the gray level characteristics of the blood vessel between the start point and the end point of the blood vessel positioned in the step S1, and constructing a blood vessel filtering weight value graph according to the calculated filtering value;
s3: in the vessel filtering weight map constructed in step S2, the Dijkstra optimal path extraction algorithm is used to complete the extraction of the central paths of the carotid artery and vertebral artery vessels.
The human vertebral artery starts from the subclavian artery, passes through the transverse apopore of the vertebra, bends towards the inner side at the back of the lateral mass of the atlas, passes through the macropore of the occipital bone and enters the cranial cavity, and is combined with the contralateral vertebral artery to form the basilar artery at the lower edge of the brain bridge, so the invention needs to position the starting point of the left and right vertebral arteries and simultaneously needs to position the basilar artery point as the termination point of the left and right vertebral arteries.
The carotid artery originates from the aortic arch and subclavian artery and branches upward into the internal and external carotid arteries. Wherein the internal carotid artery extends upwards to enter the cranial cavity through the rupture hole and enters the cavernous sinus through the subkaryoid sinus, an S-shaped bend is formed in the cavernous sinus and extends forwards to extend through the dura mater, and the branch has a posterior traffic branch, a cerebral anterior and a middle artery to participate in forming a cerebral artery loop. The invention needs to separately locate the starting point of the left and right carotid artery and the termination point of the internal carotid artery.
Step S1 of the present invention: the method comprises the following steps of automatically positioning the starting point and the end point of a carotid artery and a vertebral artery respectively according to the shape and the gray scale of a blood vessel in the cross section of a CTA image and the position characteristics, and comprises the following specific steps:
s1.1 automatic positioning of starting points of vertebral artery blood vessels and carotid artery blood vessels
S1.1.1 locating candidate levels of carotid and vertebral artery origins
Firstly, performing binarization preprocessing on CTA image data meeting requirements to distinguish human tissues and background areas in an image; then identifying a hole area with a gray value less than-450 HU in the human tissue area, scanning layer by layer from bottom to top (from the autonomous artery arch to the cranial vertex), and when the area of a single hole is more than 60mm2And less than 1200mm2When the current level is considered as the upper edge of the lung parenchyma, the level is taken as a candidate level of the starting points of the vertebral artery and the carotid artery, and the scanning is stopped;
s1.1.2 locating the starting point of vertebral artery and carotid artery
And (3) performing label extraction on all regions with the gray values larger than 150HU in the candidate layer to obtain candidate regions of vertebral artery and carotid artery blood vessels, and screening the candidate regions by using the following rules:
1) calculating the gravity center of the marked tissue, and removing the area of the gravity center which is not marked;
2) on the basis of the step 1), calculating the adjacent edge ratio of the minimum circumscribed rectangle of the remaining candidate region, and removing the region with the ratio being more than 3 or less than 1/3;
3) according to the size characteristics of the normal vertebral artery and the carotid artery, on the basis of the steps 1) and 2), further removing the candidate region with the area larger than 60mm2And less than 3mm2The area of (a);
4) calculating the gray level mean value and the variance of the remaining candidate regions one by one, taking the gravity center of the current candidate region as a seed point, taking the 2-time variance as a growth step length, and carrying out three-dimensional region growth in the upper and lower 3-layer image ranges by taking the layer of the current candidate region as a reference; judging the region grown on each layer again according to the adjacent edge ratio in the step 2), and removing the candidate regions which do not meet the conditions;
5) the cross section of the blood vessel presents a similar circular characteristic in a CTA image, and the distribution in the human body is approximately centrosymmetric, so that the circularity, central Symmetry, bounding box aspect Ratio and Area are respectively calculated for the region screened in the step 4), wherein the central Symmetry represents whether a candidate region exists in the range of 5mm above, below, left and right of the opposite side by taking the center (x direction) of the human tissue after binarization as a reference; if the distance can be found, calculating the distance between the gravity centers of all the candidate blood vessel regions in the search range and the gravity center of the current region in the y direction, and taking the corresponding minimum distance as the central symmetry degree of the candidate blood vessel region; if not, setting the central symmetry of the current blood vessel candidate region as the side length of the whole image;
6) sorting the 4 metric parameters calculated in step 5): the circularity and the area of the region are sorted from large to small, the horizontal-longitudinal ratio of the bounding boxes is sorted from small to large according to the difference value between the circularity and the area of the region and the area of the bounding boxes, the central symmetry is sorted from small to large, and the sorted order is used as the fraction of the value of the region metric: the circularity fraction is denoted ScirclarityAnd the symmetry fraction is represented as SsymmetryThe aspect ratio fraction is represented as SratioThe area fraction is represented as Sarea；
7) Defining a vessel cross-section similarity score: score w 1Scirclarity+w2*Ssymmetry+w3*Sratio+w4*SareaW1, w2, w3 and w4 respectively represent weights corresponding to the circularity fraction, the symmetry fraction, the aspect ratio fraction and the area fraction of the candidate blood vessel region; the smaller the vessel similarity score is, the more likely the region is a vessel region;
8) sorting the blood vessel similarity Score according to a sequence from small to large to obtain a candidate region ranked in the first four; firstly, defining two regions with larger areas as the initial regions of the left and right carotid arteries, and defining the other two regions as the initial regions of the left and right vertebral arteries; then distinguishing left and right carotid arteries and left and right vertebral arteries according to the obtained x coordinates of the centers of gravity of the carotid artery and vertebral artery initial regions; finally, the barycenter of the blood vessel region is used as the starting point of the left and right carotid arteries (such as points A and B in FIG. 3A) and the starting point of the left and right vertebral arteries (such as points C and D in FIG. 3A).
9) If the screened areas are smaller than 4, or the screened areas are positioned at one side of the center (x direction) of the human tissue, the scanning upwards is continued by taking the current candidate layer as the reference, and simultaneously the starting points of the carotid artery and the vertebral artery are continuously searched by returning to S1.1.1.
Since the vertebral artery originates from the subclavian artery, the carotid artery originates from the aortic arch and the subclavian artery, and the starting points of the vertebral artery and carotid artery are located in the lower half of the CTA image data that meets the requirements, the positioning of the starting points of the vertebral artery and carotid artery is performed until the lower half of the CTA data is scanned layer by layer.
S1.2 automatic definition of the end points of vertebral artery and carotid artery blood vessels
S1.2.1 automatic definition of vertebral artery vessel endpoint (i.e. basilar artery point)
S1.2.1.1 locating basilar artery point candidate slices
Scanning layer by layer (from the cranial vertex to the aortic arch direction) from top to bottom, extracting real brain tissue by taking a 120HU gray value as a threshold value, and simultaneously calculating the real brain tissue and the body tissue bounding box after binarization processing in S1.1.1; when the bounding box area of the brain tissue is sized to 1/3 of the volume tissue bounding box, the scan is stopped and the slice is taken as a candidate slice of the basilar artery.
S1.2.1.2 locating basilar artery points
And (3) performing label extraction on the region with the gray value of 150-750HU in the candidate layer to obtain a candidate region of the basilar artery, and screening the candidate region by using the following rules:
1) calculating the gravity center of the marked tissue, and removing the area of the gravity center which is not marked;
2) adopting a blood vessel enhancement function based on a Hessian matrix to perform enhancement processing on the candidate region filtered in the step 1);
3) removing the candidate area corresponding to the threshold value by taking 0.6 as the threshold value of the blood vessel enhancement;
4) taking the gravity center of the candidate region filtered in the step 3) as a seed point and 100HU as a step length, and performing two-dimensional region growth;
5) according to the size characteristics of normal basilar artery, the removal area is less than 1mm2Greater than 35mm2The blood vessel candidate region of (a);
6) calculating a bounding box of each candidate region, and removing regions with the bounding box aspect ratio larger than 4;
7) extracting the boundary of the blood vessel candidate region, calculating the maximum and minimum distances from the gravity point to the boundary point, and removing the blood vessel candidate region of which the ratio of the maximum distance to the minimum distance is more than 8;
8) taking the gravity center of the blood vessel candidate region as a seed point and 100HU as a step length, carrying out three-dimensional region growth on the upper layer image and the lower layer image, extracting a blood vessel region adjacent to the current blood vessel candidate region, and calculating a bounding box of the adjacent blood vessel region; removing the blood vessel candidate area with the aspect ratio of the bounding box being more than 4 or the longest side being more than 10 mm;
9) calculating the centering degree, the height and the area of the blood vessel candidate region screened in the step 8), wherein the centering degree represents the distance between the gravity center of the blood vessel candidate region and the center of the brain tissue; the height represents the coordinate of the gravity center y direction of the blood vessel candidate region;
10) sorting the 3 metric parameters calculated in step 9): the centering degree is sorted from small to large, the height and the area are sorted from large to small, and the sorted order is used as the fraction of the metric value of the region: the median score is denoted ScenterHeight fraction is represented as SyThe area fraction is represented as Sarea；
11) Construction of the vascular similarity Score w 1Scenter+w2*Sy+w3*SareaW1, w1 and w3 respectively represent the median score, height score and weight corresponding to the area of the candidate blood vessel region; taking the gravity center of the blood vessel candidate region with the smallest blood vessel similarity score as a basilar artery point, such as point E in FIG. 3B;
12) if no blood vessel candidate area exists after the screening in the step 8), the downward scanning is continued by taking the current layer as a reference, and meanwhile, the method returns to S1.2.1.1 to continue searching for the base artery point.
For satisfactory CTA image data, the basilar artery points are in the upper half of the data, so the basilar artery points are positioned until the upper half of the CTA data is scanned layer-by-layer.
S1.2.2 locating carotid artery end point
S1.2.2.1 locating carotid artery endpoint candidate level
Scanning layer by layer from top to bottom (from the vertex to the aortic arch direction), calculating a skull bounding box, constructing an intracranial tissue bounding box by using an area of the skull bounding box which is shrunk inwards 1/2, and counting the number of pixel points of which the internal gray value is greater than 550 HU; if all pixel points in the range of the bounding box are more than 550HU, judging that the current layer is a cranial vertex region, not judging at the moment, and continuing to scan downwards; when the gray values of all pixels in the bounding box are less than 550HU, the scanning is considered to enter the brain tissue area; and after the brain tissue area appears, judging the proportion of the number of the bone pixel points in the bounding box to the number of all the pixel points in the tissue bounding box, if the proportion is more than 0.005, stopping searching, and taking the layer as a candidate layer of the internal carotid artery endpoint.
S1.2.2.2 locating carotid artery endpoint
1) Calculating the mean value CT of the gray level of the blood vessel region where the carotid artery starting point positioned in S1.1 is locatedmeanSum standard deviation CTstdUsing carotid artery starting point as seed point and CTmean+CTstd、CTmean-CTstdPerforming unidirectional upward three-dimensional region growth as an upper threshold and a lower threshold until a candidate layer of the internal carotid artery endpoint is reached, and marking a candidate blood vessel region in the candidate layer;
2) in the layer where the internal carotid artery endpoint is located, searching pixel points with the gray value higher than 750HU, taking the pixel points as bone tissue seed points, extracting all tissues communicated with the seed points within the range of 120-3071HU through a two-dimensional region growing mode, and removing the candidate blood vessel region adhered to the bone in the step 1);
3) calculating the central Symmetry, the bounding box aspect Ratio and the region Area of the blood vessel candidate region obtained after screening in the step 2), wherein the definition of the central Symmetry is the same as that of the step 5) in the step S1.1.2;
4) sorting the 3 metric parameters calculated in step 3): the central symmetry degrees are sorted from small to large, the horizontal-vertical ratio of the bounding boxes is sorted from small to large according to the difference value of the horizontal-vertical ratio of the bounding boxes and 1, and the area of the area is sorted from large to small. Using the sorted order as the region metric valueThe fraction of (c): the central symmetry fraction is denoted SsymmetryAnd the horizontal-vertical ratio fraction of the bounding box is expressed as SratioAnd the area fraction of the region is represented as Sarea；
5) Defining a vessel cross-section similarity score: score w 1Ssymmetry+w2*Sratio+w3*Sarea. W1, w2 and w3 respectively represent weights corresponding to the central symmetry fraction, bounding box aspect ratio fraction and region area fraction of the candidate blood vessel region. The smaller the vessel similarity score is, the more likely the region is a vessel region;
6) sorting the blood vessel section similarity scores Score in a descending order, and defining two blood vessel candidate regions with the highest scores as the corresponding gravity centers as the end points of the left and right carotid arteries according to the x coordinate direction, such as points F and G in FIG. 3C;
7) if the candidate blood vessel regions screened in the step 2) are less than 2, continuing to scan downwards by taking the current layer as a reference, and simultaneously returning to the step 2) to continue searching for the stiff artery end point.
For satisfactory CTA image data, the carotid endpoint is located at the top half of the data, so the carotid endpoint is located until the top half of the CTA data is scanned layer-by-layer. .
Step S2 of the present invention: and between the starting point and the end point of the blood vessel positioned in the step S1, carrying out Raycasting-based multi-scale gradient normalized blood vessel filtering on the region conforming to the gray level characteristics of the blood vessel, and constructing a blood vessel filtering weight value map.
S2.1 vascular Pre-extraction
Respectively calculating the mean value CT of the gray levels of the corresponding regions according to the carotid artery and vertebral artery blood vessel regions positioned in S1.1meanSum standard deviation CTstdUsing carotid and vertebral artery starting points located in S1.1 as seed points, CTmean+2*CTstd、CTmean-2*CTstdPerforming unidirectional upward three-dimensional region growth as an upper threshold and a lower threshold; the carotid artery grows to the level of the carotid artery terminal point, and the vertebral artery grows to the level of the vertebral artery terminal point;
s2.2 Raycasting-based multi-scale gradient normalization vascular filtering
In the CTA image, the cross section of the blood vessel has a feature of a shape similar to a circle, and the gray scale change has a feature of a gaussian-like distribution (the gray scale value at the center of the blood vessel is high, and the gray scale value gradually decreases with the increase of the radius with the center as the center of the circle). The multi-scale gradient normalization blood vessel filtering based on Raycasting just utilizes the two characteristics of blood vessels to calculate the blood vessel filtering value of each pixel point in the pre-extracted blood vessel region in S2.1, and the specific steps are as follows:
1) since the cross-sectional dimension of the blood vessel varies in its course, it is necessary to calculate the filtered value of the blood vessel at different radius scales (as shown in fig. 2), R e (R ∈)min,Rmax](in the subsequent step with RminAnd RmaxRepresent the minimum and maximum radius dimensions, no longer distinguishing between carotid and vertebral arteries);
2) taking any one blood vessel region point pre-extracted in S2.1 as a circle center, respectively calculating the projection radius scale as R and the gradient response V of the circle center pointRMinimum V of gradient response on the 0 to R radius scaleR,min，RminTo RmaxMaximum value V at radius scaleR,max；
3) Defining the normalized gradient response at the radius scale R as ER＝(VR-VR,min)/VR,maxIf the current point is the center point of the blood vessel, ERAbout equal to 1; if it is a non-vascular point, ERAbout 0;
4) repeating steps 2) -3), calculating a plurality of projection radius scales RminTo RmaxE ofRAnd will be at different scales ERThe maximum value of (2) is used as a final blood vessel filtering value of the point;
s2.3 construction of vascular filtering weight map
For each blood vessel point extracted in S2.1, a blood vessel filtering value can be calculated through S2.2, and a blood vessel filtering weight value of the blood vessel point is defined as:
wherein,andrespectively, the reciprocal of the blood vessel filtering value calculated in S2.2 for two adjacent points, Dist is the physical distance between the two points.
And constructing a blood vessel filtering value weight map according to the calculated blood vessel filtering weights of all the blood vessel points.
Step S3 of the present invention: in the vessel filtering weight map constructed in step S2, the Dijkstra optimal path extraction algorithm is used to complete the extraction of the central paths of the carotid artery and vertebral artery vessels.
Obtaining the shortest central path between the starting point and the ending point of the blood vessel by adopting an optimal path extraction algorithm of Dijkstra, and firstly, defining an energy function between the starting point and the ending point of the blood vessel positioned in the step S1:
E＝∫(w(s)+ε)ds
wherein w (S) is the vessel filtering enhancement weight calculated in S2.3, epsilon is a regular term, and S is a path between a starting point and an end point.
And then, selecting a path with the minimum blood vessel filtering weight sum in all paths between the starting point and the end point of the blood vessel as a blood vessel center path.
Fig. 3D is a diagram showing the extracted right carotid artery central path and its corresponding CPR (curved surface reconstruction) result, fig. 3E is a diagram showing the extracted left carotid artery central path and its corresponding CPR (curved surface reconstruction) result, fig. 3F is a diagram showing the extracted right vertebral artery central path and its corresponding CPR (curved surface reconstruction) result, and fig. 3G is a diagram showing the extracted left vertebral artery central path and its corresponding CPR (curved surface reconstruction) result.
Compared with the prior art, the invention has the advantages that:
1. the automatic positioning of the blood vessel is realized without manual participation of a user;
2. the extracted central line is accurate, and the later adjustment of a user is not needed;
3. the method has high running speed and meets the real-time requirement.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for automatically extracting a central path of a head-neck blood vessel in a CTA image is suitable for CTA image data scanned from an aortic arch to a cranial vertex, and is characterized in that: it comprises the following steps:
s1: automatically positioning the starting point and the end point of the carotid artery and the vertebral artery respectively according to the shape, the gray scale and the position characteristics of the blood vessel in the cross section of the CTA image;
s2: performing Raycasting-based multi-scale gradient normalized blood vessel filtering on the region according with the gray level characteristics of the blood vessel between the start point and the end point of the blood vessel positioned in the step S1 to construct a blood vessel filtering weight value graph;
s3: in the vessel filtering weight map constructed in step S2, the Dijkstra optimal path extraction algorithm is used to complete the extraction of the central paths of the carotid artery and vertebral artery vessels.
2. The method of claim 1 for automatically extracting the central path of head and neck blood vessels in CTA image, which comprises: the step S1 is a method for automatically locating the starting points of the carotid artery and the vertebral artery:
s1.1.1 locating candidate levels of carotid and vertebral artery origins
Carrying out binarization preprocessing on CTA image data, and distinguishing human body tissues and background areas in the image; then identifying a hole area with the gray value less than-450 HU in the human tissue area, and scanning layer by layer from bottom to top, when the area of a single hole is more than 60mm2And less than 1200mm2When, consider the current layerThe surface is the upper edge of the lung parenchyma, and the layer is taken as a candidate surface of the starting points of the vertebral artery and the carotid artery at the moment, and the scanning is stopped;
s1.1.2 locating the starting point of vertebral artery and carotid artery
And marking and extracting all regions with the gray values larger than 150HU in the candidate layer to obtain candidate regions of the vertebral artery and the carotid artery blood vessel, screening the candidate regions and determining the starting points of the vertebral artery blood vessel and the carotid artery blood vessel.
3. The method of claim 2 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein: the method for screening candidate regions in step S1.1.2 is as follows:
1) calculating the gravity center of the marked tissue, and removing the area of the gravity center which is not marked;
2) on the basis of the step 1), calculating the adjacent edge ratio of the minimum circumscribed rectangle of the remaining candidate region, and removing the region with the ratio being more than 3 or less than 1/3;
3) according to the size characteristics of the normal vertebral artery and the carotid artery, on the basis of the steps 1) and 2), further removing the candidate region with the area larger than 60mm2And less than 3mm2The area of (a);
4) calculating the gray level mean value and the variance of the remaining candidate regions one by one, taking the gravity center of the current candidate region as a seed point, taking the 2-time variance as a growth step length, and carrying out three-dimensional region growth in the upper and lower 3-layer image ranges by taking the layer of the current candidate region as a reference; judging the region grown on each layer again according to the adjacent edge ratio in the step 2), and removing the candidate regions which do not meet the conditions;
5) the cross section of the blood vessel presents a similar circular characteristic in a CTA image, and the distribution in the human body is approximately centrosymmetric, so that the circularity, central Symmetry, bounding box aspect Ratio and Area are respectively calculated for the region screened in the step 4), wherein the central Symmetry represents whether a candidate region exists in the range of 5mm above, below, left and right of the opposite side by taking the center (x direction) of the human tissue after binarization as a reference; if the distance can be found, calculating the distance between the gravity centers of all the candidate blood vessel regions in the search range and the gravity center of the current region in the y direction, and taking the corresponding minimum distance as the central symmetry degree of the candidate blood vessel region; if not, setting the central symmetry of the current blood vessel candidate region as the side length of the whole image;
6) sorting the 4 metric parameters calculated in step 5): the circularity and the area are sorted from large to small, the horizontal-longitudinal ratio is sorted from small to large according to the difference value between the circularity and the area and 1, the symmetry is sorted from small to large, and the sorted order is used as the fraction of the area metric value: the circularity fraction is denoted ScirclarityAnd the symmetry fraction is represented as SsymmetryThe aspect ratio fraction is represented as SratioThe area fraction is represented as Sarea；
7) Defining a vessel cross-section similarity score: score w 1Scirclarity+w2*Ssymmetry+w3*Sratio+w4*SareaW1, w2, w3 and w4 respectively represent weights corresponding to the circularity fraction, the symmetry fraction, the aspect ratio fraction and the area fraction of the candidate blood vessel region; the smaller the vessel similarity score is, the more likely the region is a vessel region;
8) sorting the blood vessel similarity Score according to a sequence from small to large to obtain a candidate region ranked in the first four; firstly, defining two regions with larger areas as the initial regions of the left and right carotid arteries, and defining the other two regions as the initial regions of the left and right vertebral arteries; then distinguishing left and right carotid arteries and left and right vertebral arteries according to the obtained x coordinates of the centers of gravity of the carotid artery and vertebral artery initial regions; finally, the gravity center of the blood vessel region is used as the starting points of the left and right carotid arteries and the left and right vertebral arteries;
9) if the screened areas are smaller than 4, or the screened areas are positioned at one side of the center (x direction) of the human tissue, the scanning upwards is continued by taking the current candidate layer as the reference, and simultaneously the starting points of the carotid artery and the vertebral artery are continuously searched by returning to S1.1.1.
4. The method of claim 3 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein: the step S1 is a method for automatically positioning the vertebral artery terminal:
the terminal, basilar artery, point of the vertebral artery;
s1.2.1.1 locating basilar artery point candidate slices
Scanning layer by layer (in the direction from the cranial vertex to the aortic arch) from top to bottom, extracting real brain tissue by taking a 120HU gray value as a threshold value, and simultaneously calculating the real brain tissue and the body tissue bounding box after binarization processing in S1.1.1; when the bounding box area of the brain tissue is 1/3 of the volume tissue bounding box, stopping scanning and taking the layer as a candidate layer of the basilar artery;
s1.2.1.2 locating the basilar artery point, i.e. the end point of the vertebral artery
And (4) performing label extraction on the region with the gray value of 150-750HU in the candidate layer to obtain a candidate region of the basilar artery, and screening the candidate region to determine the basilar artery point.
5. The method of claim 4 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein: the method for screening the candidate regions to determine the basilar artery points in step S1.2.1.2 is as follows:
1) calculating the gravity center of the marked tissue, and removing the area of the gravity center which is not marked;
2) adopting a blood vessel enhancement function based on a Hessian matrix to perform enhancement processing on the candidate region filtered in the step 1);
3) removing the candidate area corresponding to the threshold value by taking 0.6 as the threshold value of the blood vessel enhancement;
4) taking the gravity center of the candidate region filtered in the step 3) as a seed point and 100HU as a step length, and performing two-dimensional region growth;
5) according to the size characteristics of normal basilar artery, the removal area is less than 1mm2Greater than 35mm2The blood vessel candidate region of (a);
6) calculating a bounding box of each candidate region, and removing regions with the bounding box aspect ratio larger than 4;
7) extracting the boundary of the blood vessel candidate region, calculating the maximum and minimum distances from the gravity point to the boundary point, and removing the blood vessel candidate region of which the ratio of the maximum distance to the minimum distance is more than 8;
8) taking the gravity center of the blood vessel candidate region as a seed point and 100HU as a step length, carrying out three-dimensional region growth on the upper layer image and the lower layer image, extracting a blood vessel region adjacent to the current blood vessel candidate region, and calculating a bounding box of the adjacent blood vessel region; removing the blood vessel candidate area with the aspect ratio of the bounding box being more than 4 or the longest side being more than 10 mm;
9) calculating the centering degree, the height and the area of the blood vessel candidate region screened in the step 8), wherein the centering degree represents the distance between the gravity center of the blood vessel candidate region and the center of the brain tissue; the height represents the coordinate of the gravity center y direction of the blood vessel candidate region;
10) sorting the 3 metric parameters calculated in step 9): the centering degree is sorted from small to large, the height and the area are sorted from large to small, and the sorted order is used as the fraction of the metric value of the region: the median score is denoted ScenterHeight fraction Sy and area fraction Sarea；
11) Construction of the vascular similarity Score w 1Scenter+w2*Sy+w3*SareaW1, w1 and w3 respectively represent the median score, height score and weight corresponding to the area of the candidate blood vessel region; taking the gravity center of the blood vessel candidate region with the minimum blood vessel similarity score as a basilar artery point;
12) if no blood vessel candidate area exists after the screening in the step 8), the downward scanning is continued by taking the current layer as a reference, and meanwhile, the method returns to S1.2.1.1 to continue searching for the base artery point.
6. The method of claim 5 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein: the step S1 is a method for automatically locating the carotid artery end point:
the end point of the carotid artery is the end point of the internal carotid artery,
s1.2.2.1 locating carotid artery endpoint candidate level
Scanning layer by layer from top to bottom (from the vertex to the aortic arch direction), calculating a skull bounding box, constructing an intracranial tissue bounding box by using an area of the skull bounding box which is shrunk inwards 1/2, and counting the number of pixel points of which the internal gray value is greater than 550 HU; if all pixel points in the range of the bounding box are more than 550HU, judging that the current layer is a cranial vertex region, not judging at the moment, and continuing to scan downwards; when the gray values of all pixels in the bounding box are less than 550HU, the scanning is considered to enter the brain tissue area; after a brain tissue area appears, judging the proportion of the number of bone pixel points in the bounding box to the number of all pixel points in the tissue bounding box in which the bone pixel points are located layer by layer, if the proportion is more than 0.005, stopping searching, and using the layer as a candidate layer of an internal carotid artery endpoint;
s1.2.2.2 locating carotid artery endpoint
1) Calculating the mean value CT of the gray level of the blood vessel region where the carotid artery starting point positioned in S1.1 is locatedmeanSum standard deviation CTstdUsing carotid artery starting point as seed point and CTmean+CTstd、CTmean-CTstdPerforming unidirectional upward three-dimensional region growth as an upper threshold and a lower threshold until a candidate layer of the internal carotid artery endpoint is reached, and marking a candidate blood vessel region in the candidate layer;
2) in the layer where the internal carotid artery endpoint is located, searching pixel points with the gray value higher than 750HU, taking the pixel points as bone tissue seed points, extracting all tissues communicated with the seed points within the range of 120-3071HU through a two-dimensional region growing mode, and removing the candidate blood vessel region adhered to the bone in the step 1);
3) calculating the central Symmetry, the bounding box aspect Ratio and the Area of the blood vessel candidate region obtained after screening in the step 2), wherein the Symmetry is the same as the definition of the step 5) in the step S1.1.2;
4) sorting the 3 metric parameters calculated in step 3): the central symmetry degrees are sorted from small to large, the horizontal-vertical ratio of the bounding boxes is sorted from small to large according to the difference value of the horizontal-vertical ratio of the bounding boxes and 1, the area of the region is sorted from large to small, and the sorted order is used as the fraction of the measurement value of the region: the central symmetry fraction is denoted SsymmetryThe enclosure is horizontal and verticalThe ratio score is expressed as SratioAnd the area fraction of the region is represented as Sarea；
5) Defining a vessel cross-section similarity score: score w 1Ssymmetry+w2*Sratio+w3*Sarea(ii) a W1, w2 and w3 respectively represent weights corresponding to the central symmetry fraction, bounding box aspect ratio fraction and region area fraction of the candidate blood vessel region; the smaller the vessel similarity score is, the more likely the region is a vessel region;
6) sorting the vessel section similarity scores Score in a descending order, and defining two vessel candidate regions with the highest scores as corresponding gravity centers as end points of the left and right carotid arteries according to the x coordinate direction;
7) if the candidate blood vessel regions screened in the step 2) are less than 2, continuing to scan downwards by taking the current layer as a reference, and simultaneously returning to the step 2) to continue searching for the stiff artery end point.
7. The method of claim 6 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein:
in step S2, a multi-scale gradient normalization blood vessel filtering based on Raycasting is performed on the region conforming to the gray level feature of the blood vessel between the starting point and the ending point of the blood vessel, and the method for constructing the blood vessel filtering weight map is as follows:
s2.1 vascular Pre-extraction
Respectively calculating the mean value CT of the gray levels of the corresponding regions according to the carotid artery and vertebral artery blood vessel regions positioned in S1.1meanSum standard deviation CTstdUsing carotid and vertebral artery starting points located in S1.1 as seed points, CTmean+2*CTstd、CTmean-2*CTstdPerforming unidirectional upward three-dimensional region growth as an upper threshold and a lower threshold; the carotid artery grows to the level of the carotid artery terminal point, and the vertebral artery grows to the level of the vertebral artery terminal point;
s2.2, based on Raycasting multi-scale gradient normalization blood vessel filtering, calculating a blood vessel filtering value of each pixel point in the pre-extracted blood vessel region in S2.1;
s2.3 construction of vascular filtering weight map
For each blood vessel point extracted in S2.1, a blood vessel filtering value can be calculated through S2.2, and a blood vessel filtering weight value of the blood vessel point is defined as:
wherein,andrespectively calculating the reciprocal of the blood vessel filtering value calculated in S2.2 for two adjacent points, and Dist is the physical distance between the two points;
and constructing a blood vessel filtering value weight map according to the calculated blood vessel filtering weights of all the blood vessel points.
8. The method of claim 7 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein: s2.2, based on Raycasting multi-scale gradient normalization blood vessel filtering, calculating a blood vessel filtering value of each pixel point in a pre-extracted blood vessel region in S2.1, and specifically comprising the following steps:
1) since the cross-sectional dimension of a blood vessel varies in its course, it is necessary to calculate the filtered value of the blood vessel at different radius scales, R e (R ∈)min,Rmax](in the subsequent step with RminAnd RmaxRepresent the minimum and maximum radius dimensions, no longer distinguishing between carotid and vertebral arteries);
2) taking any one blood vessel region point pre-extracted in S2.1 as a circle center, respectively calculating the projection radius scale as R and the gradient response V of the circle center pointRMinimum V of gradient response on the 0 to R radius scaleR,min，RminTo RmaxMaximum value V at radius scaleR,max；
3) Defining the normalized gradient response at the radius scale R as ER＝(VR-VR,min)/VR,maxIf the current point is the center point of the blood vessel, ERAbout equal to 1; if it is a non-vascular point, ERAbout 0;
4) repeating steps 2) -3), calculating a plurality of projection radius scales RminTo RmaxE ofRAnd will be at different scales ERThe maximum value of (a) is used as the final blood vessel filtering value at the point.
9. The method of claim 8 for automatically extracting the central path of the head and neck blood vessels in the CTA image, wherein: the step S3: in the vessel filtering weight map constructed in step S2, the method for extracting the central paths of the carotid artery and the vertebral artery by using Dijkstra optimal path extraction algorithm is as follows:
s3.1: defining an energy function between the start and end points of the blood vessel located in step S1:
E＝∫(w(s)+ε)ds
wherein w (S) is the blood vessel filtering enhancement weight calculated in S2.3, epsilon is a regular term, and S is a path between a starting point and an end point;
and S3.2, selecting the path with the minimum blood vessel filtering weight sum in all paths between the starting point and the end point of the blood vessel as the blood vessel central path.
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