CN110338830A - A method for automatically extracting the central path of head and neck vessels in CTA images - Google Patents

A method for automatically extracting the central path of head and neck vessels in CTA images Download PDF

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CN110338830A
CN110338830A CN201910693725.6A CN201910693725A CN110338830A CN 110338830 A CN110338830 A CN 110338830A CN 201910693725 A CN201910693725 A CN 201910693725A CN 110338830 A CN110338830 A CN 110338830A
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孙奇
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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

自动提取CTA影像中头颈血管中心路径的方法A method for automatically extracting the central path of head and neck vessels in CTA images

技术领域technical field

本发明涉及一种自动提取CTA影像中头颈血管中心路径的方法。本发明属于医学影像处理技术领域。The invention relates to a method for automatically extracting the central path of head and neck blood vessels in CTA images. The invention belongs to the technical field of medical image processing.

背景技术Background technique

脑血管疾病是目前威胁人类健康的主要疾病之一,临床中主要通过对CTA影像(即非创伤性血管成像检查)后的图像进行分析,提取血管的中心路径来获取血管的三维结构信息,辅助医生判断血管病变程度。但是,由于血管在人体中的分布非常复杂,血管经常穿行于骨骼之中,同时在CTA影像后生成的图像中骨和血管均呈现高亮的灰度特征,这给血管中心路径的提取、病变分析带来很大的困难。Cerebrovascular disease is one of the main diseases that threaten human health. In clinical practice, the three-dimensional structural information of blood vessels is obtained by analyzing the images after CTA imaging (ie, non-invasive vascular imaging examination), and extracting the central path of blood vessels. Doctors determine the degree of vascular disease. However, due to the complex distribution of blood vessels in the human body, blood vessels often pass through the bones, and both the bones and blood vessels show bright grayscale features in the images generated after CTA imaging, which makes the extraction of the central path of the blood vessels and the pathological changes. Analysis brings great difficulty.

目前,对CTA影像后生成的图像进行血管分析时,通常先利用减影技术去除CTA影像中的骨骼,获取血管区域,然后再通过手动的方式定义血管的起始点和终止点,获取血管的中心路径。这种方法的弊端是:由于减影操作需要对患者进行两次扫描(CT和CTA扫描),在两次扫描的间隔中患者很容易发生位移,这样就会造成减影后的血管结构出现缺失或断裂的情况,进而对后续血管的病变分析造成影响。At present, when performing blood vessel analysis on images generated after CTA images, subtraction technology is usually used to remove the bones in the CTA images to obtain the blood vessel area, and then manually define the start point and end point of the blood vessel to obtain the center of the blood vessel. path. The disadvantage of this method is: since the subtraction operation requires two scans of the patient (CT and CTA scans), the patient is easily displaced between the two scans, which will result in the loss of the vascular structure after subtraction. or rupture, which will affect the subsequent analysis of vascular lesions.

利用基于Hessian的血管增强法获取血管中心路径的方法,虽然可以在CTA图像中直接提取血管的中心路径,但是对于形态和周围组织较为复杂的血管,例如椎骨和颈内动脉的入颅段,通常效果不是很理想,而且也需要医生手动定义血管的起始点和终止点,给医生的分析工作带来一定的负担。Using the Hessian-based vascular enhancement method to obtain the central path of the blood vessel, although the central path of the blood vessel can be directly extracted from the CTA image, for blood vessels with complex shapes and surrounding tissues, such as the vertebrae and the cranial segment of the internal carotid artery, usually The effect is not very ideal, and the doctor needs to manually define the starting point and ending point of the blood vessel, which brings a certain burden to the doctor's analysis work.

发明内容SUMMARY OF THE INVENTION

鉴于上述原因,本发明的目的是提供一种自动提取CTA影像中头颈血管即椎动脉血管和颈动脉血管中心路径的方法。In view of the above reasons, the purpose of the present invention is to provide a method for automatically extracting the central paths of the head and neck vessels, ie, the vertebral artery vessels and the carotid vessels, in CTA images.

为实现上述目的,本发明采用以下技术方案:一种自动提取CTA影像中头颈血管中心路径的方法,该方法适用从主动脉弓扫描至颅顶的CTA影像数据,其特征在于:它包括如下步骤:In order to achieve the above object, the present invention adopts the following technical scheme: a method for automatically extracting the central path of the head and neck blood vessels in the CTA image, the method is applicable to the CTA image data scanned from the aortic arch to the cranial top, and is characterized in that: it comprises the following steps:

S1:依据血管在CTA影像横断面的形状、灰度及位置特征,分别对颈动脉和椎动脉的起点和终点进行自动定位;S1: According to the shape, gray level and position characteristics of the blood vessels in the cross-section of the CTA image, the starting point and the end point of the carotid artery and the vertebral artery are automatically located respectively;

S2:在步骤S1定位的血管起点和终点之间,对符合血管灰度特征的区域进行基于Raycasting的多尺度梯度归一化血管滤波,构建血管滤波权值图;S2: Between the start point and the end point of the blood vessel located in step S1, perform multi-scale gradient normalization blood vessel filtering based on Raycasting on the region conforming to the grayscale characteristics of the blood vessel, and construct a blood vessel filtering weight map;

S3:在步骤S2构建的血管滤波权值图中,采用Dijkstra最优路径提取算法完成对颈动脉和椎动脉血管中心路径的提取。S3: In the blood vessel filtering weight map constructed in step S2, the Dijkstra optimal path extraction algorithm is used to complete the extraction of the central path of the carotid artery and the vertebral artery.

本发明的优点:实现血管的自动定位,无需用户手动参与;提取的中心线准确,无需用户后期调整;方法运行速度快,满足实时性需求。The advantages of the invention are as follows: the automatic positioning of the blood vessel is realized without the user's manual participation; the extracted centerline is accurate, and the user does not need to adjust later; the method runs quickly and meets the real-time requirement.

附图说明Description of drawings

图1是本发明自动提取头颈血管中心路径的方法流程图;Fig. 1 is the flow chart of the method for automatically extracting the central path of head and neck blood vessels according to the present invention;

图2是基于Raycasting(光线投射)的多尺度梯度归一化血管滤波图;Fig. 2 is a multi-scale gradient normalized blood vessel filtering diagram based on Raycasting (ray casting);

图3A是步骤S1.1.2后定位的颈动脉和椎动脉的起点(绿色标识点);Figure 3A is the origin of the carotid artery and vertebral artery (green marking point) located after step S1.1.2;

图3B是步骤S1.2.1.2后定位的椎动脉终点(绿色标识点);Figure 3B is the end point of the vertebral artery (green marking point) located after step S1.2.1.2;

图3C是步骤S1.2.2.2后定位的颈动脉终点(绿色标识点);Figure 3C is the carotid artery end point (green marking point) located after step S1.2.2.2;

图3D是步骤S3.2后提取的右侧颈动脉中心路径及其对应的CPR(曲面重建)结果图;Fig. 3D is the right carotid artery central path extracted after step S3.2 and the corresponding CPR (surface reconstruction) result diagram;

图3E是步骤S3.2后提取的左侧颈动脉中心路径及其对应的CPR(曲面重建)结果图;Fig. 3E is the left carotid artery central path extracted after step S3.2 and its corresponding CPR (surface reconstruction) result diagram;

图3F是步骤S3.2后提取的右侧椎动脉中心路径及其对应的CPR(曲面重建)结果图;Fig. 3F is the right vertebral artery central path extracted after step S3.2 and the corresponding CPR (surface reconstruction) result diagram;

图3G是步骤S3.2后提取的左侧椎动脉中心路径及其对应的CPR(曲面重建)结果图。FIG. 3G is a graph of the central path of the left vertebral artery extracted after step S3.2 and its corresponding CPR (surface reconstruction) result.

具体实施方式Detailed ways

下面结合附图和实施例对本发明进行详细说明。需要说明的是,可以对此处公开的实施例做出各种修改,因此,说明书中公开的实施例不应该视为对本发明的限制,而仅是作为实施例的范例,其目的是使本发明的特征显而易见。The present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be noted that various modifications can be made to the embodiments disclosed herein, therefore, the embodiments disclosed in the specification should not be regarded as limitations of the present invention, but only as examples of the embodiments, the purpose of which is to make the present invention The features of the invention are obvious.

如图1所示,本发明提供的自动提取CTA影像中头颈血管(椎动脉血管和颈动脉血管)中心路径的方法适用从主动脉弓扫描至颅顶的CTA影像数据(下文描述为符合要求的CTA影像数据)。本发明提供的方法主要包括三个关键步骤:As shown in FIG. 1 , the method for automatically extracting the central path of head and neck blood vessels (vertebral artery blood vessels and carotid arteries) in a CTA image provided by the present invention is applicable to the CTA image data scanned from the aortic arch to the top of the skull (described below as the CTA image that meets the requirements) data). The method provided by the present invention mainly includes three key steps:

S1:依据血管在CTA影像横断面的形状、灰度及位置特征,分别对颈动脉和椎动脉的起点和终点进行自动定位;S1: According to the shape, gray level and position characteristics of the blood vessels in the cross-section of the CTA image, the starting point and the end point of the carotid artery and the vertebral artery are automatically located respectively;

S2:在步骤S1定位的血管起点和终点之间,对符合血管灰度特征的区域进行基于Raycasting的多尺度梯度归一化血管滤波,依据计算出的滤波值构建血管滤波权值图;S2: between the start point and the end point of the blood vessel located in step S1, perform multi-scale gradient normalized blood vessel filtering based on Raycasting on the region that conforms to the grayscale characteristics of the blood vessel, and construct a blood vessel filtering weight map according to the calculated filter value;

S3:在步骤S2构建的血管滤波权值图中,采用Dijkstra最优路径提取算法完成对颈动脉和椎动脉血管中心路径的提取。S3: In the blood vessel filtering weight map constructed in step S2, the Dijkstra optimal path extraction algorithm is used to complete the extraction of the central path of the carotid artery and the vertebral artery.

人体椎动脉起于锁骨下动脉,穿行于椎骨的横突孔,在寰椎侧块后方向内侧弯曲,穿经枕骨大孔进入颅腔,在脑桥下缘,与对侧椎动脉联合形成基底动脉,因此本发明需要定位左、右椎动脉的起始点,同时需要定位基底动脉点作为左、右椎动脉的终止点。The human vertebral artery originates from the subclavian artery, passes through the transverse foramen of the vertebrae, bends medially after the lateral mass of the atlas, passes through the foramen magnum into the cranial cavity, and joins with the contralateral vertebral artery to form the basilar artery at the lower border of the pons. Therefore, the present invention needs to locate the starting points of the left and right vertebral arteries, and simultaneously needs to locate the basilar artery points as the termination points of the left and right vertebral arteries.

颈动脉起于主动脉弓和锁骨下动脉,向上产生颈内动脉和颈外动脉两个分支。其中颈内动脉向上延伸经破裂孔入颅腔,并经岩下窦入海绵窦,在海绵窦内形成一S形弯曲,再向前延伸穿过脑硬膜,分支有后交通支、大脑前和中动脉,参与构成大脑动脉环。本发明需要分别定位左、右颈动脉的起始点和颈内动脉的终止点。The carotid arteries arise from the aortic arch and the subclavian arteries, and ascend upward to give rise to two branches, the internal carotid artery and the external carotid artery. The internal carotid artery extends upward through the ruptured hole into the cranial cavity, and enters the cavernous sinus through the inferior petrosal sinus, forming an S-shaped curve in the cavernous sinus, and then extends forward through the dura mater. Arteries that participate in the formation of the cerebral arterial ring. The present invention needs to locate the starting point of the left and right carotid arteries and the ending point of the internal carotid artery, respectively.

本发明步骤S1:依据血管在CTA影像横断面的形状、灰度以位置特征,分别对颈动脉和椎动脉的起点和终点进行自动定位,具体步骤如下:Step S1 of the present invention: According to the shape of the blood vessel in the cross-section of the CTA image, the gray level and the positional feature, the starting point and the end point of the carotid artery and the vertebral artery are respectively automatically positioned, and the specific steps are as follows:

S1.1自动定位椎动脉血管、颈动脉血管的起点S1.1 Automatically locate the origin of the vertebral artery and carotid artery

S1.1.1定位颈动脉和椎动脉起点的候选层面S1.1.1 Locating candidate slices for the origin of the carotid and vertebral arteries

首先,对符合要求的CTA影像数据进行二值化预处理,区分影像中人体组织和背景区域;然后在人体组织区域内识别灰度值小于-450HU的孔洞区域,并自下而上(自主动脉弓至颅顶方向)逐层进行扫描,当单个孔洞面积大于60mm2并且小于1200mm2时,认为当前层面为肺实质的上边缘,此时将该层作为椎动脉和颈动脉起点的候选层面,并停止扫描;First, perform binarization preprocessing on the CTA image data that meets the requirements to distinguish human tissue and background areas in the image; The direction of the top of the skull) is scanned layer by layer. When the area of a single hole is greater than 60mm2 and less than 1200mm2, the current layer is considered to be the upper edge of the lung parenchyma. At this time, this layer is used as a candidate layer for the origin of the vertebral artery and carotid artery, and stop scanning;

S1.1.2定位椎动脉血管、颈动脉血管的起始点S1.1.2 Locate the origin of the vertebral and carotid vessels

在候选层面中对所有灰度值大于150HU的区域进行标记提取,获得椎动脉和颈动脉血管的候选区域,并使用如下规则对候选区域进行筛选:In the candidate layer, all regions with gray value greater than 150HU are marked and extracted to obtain the candidate regions of the vertebral artery and carotid artery, and the candidate regions are screened using the following rules:

1)计算标记组织的重心,去除重心未被标记的区域;1) Calculate the center of gravity of the marked tissue, and remove the unmarked area of the center of gravity;

2)在步骤1)的基础上,计算剩余候选区域最小外接矩形的邻边比,去除比值大于3或者小于1/3的区域;2) On the basis of step 1), calculate the adjacent side ratio of the minimum circumscribed rectangle of the remaining candidate area, and remove the area with the ratio greater than 3 or less than 1/3;

3)根据正常椎动脉和颈动脉的尺寸特征,在步骤1)和2)的基础上,进一步去除候选区域面积大于60mm2和小于3mm2的区域;3) According to the size characteristics of the normal vertebral artery and the carotid artery, on the basis of steps 1) and 2), further remove the area of the candidate area greater than 60 mm 2 and less than 3 mm 2 ;

4)逐一计算剩余候选区域的灰度均值和方差,并以当前候选区域的重心为种子点,以2倍方差为生长步长,以当前候选区域所在层为基准的上、下3层图像范围内进行三维区域生长;对每一层生长出的区域再次依据步骤2)中的邻边比进行判定,去除不满足条件的候选区域;4) Calculate the grayscale mean and variance of the remaining candidate regions one by one, and take the center of gravity of the current candidate region as the seed point, take 2 times the variance as the growth step, and take the layer where the current candidate region is located as the benchmark for the upper and lower 3-layer image ranges Carry out three-dimensional region growth in the interior; the region grown from each layer is judged again according to the adjacent edge ratio in step 2), and the candidate regions that do not meet the conditions are removed;

5)血管截面在CTA影像中呈现为类圆形特征,同时在人体中的分布是近似中心对称的,因此对步骤4)筛选后的区域分别计算圆形度Circlarity、中心对称度Symmetry、包围盒横纵比Ratio以及区域面积Area,其中,中心对称度表示以二值化后的人体组织中心(x方向)为基准,寻找对侧上下左右5mm范围内是否存在候选区域;如果能找到,则计算搜索范围内所有血管候选区域重心与当前区域重心在y方向的距离,并将对应的最小距离作为当前血管候选区域的中心对称度;如果没找到,则将当前血管候选区域的中心对称度设置为整个图像的边长;5) The cross-section of the blood vessel appears as a round-like feature in the CTA image, and the distribution in the human body is approximately centrosymmetric. Therefore, the Circularity, Symmetry, and Bounding Box are calculated for the areas screened in step 4). Aspect ratio Ratio and area area Area, among which, the center symmetry indicates that the center of the human tissue after binarization (x direction) is used as the benchmark to find out whether there is a candidate area within 5mm of the opposite side, up, down, left, and right; if it can be found, calculate The distance between the center of gravity of all blood vessel candidate regions in the search range and the center of gravity of the current region in the y direction, and the corresponding minimum distance is taken as the center symmetry degree of the current blood vessel candidate area; if not found, the center symmetry degree of the current blood vessel candidate area is set to the side length of the entire image;

6)对步骤5)中计算出的4种度量参数进行排序:其中圆形度和区域面积按照从大到小进行排序,包围盒横纵比按照与1的差值从小到大进行排序,中心对称度按照从小到大的顺序进行排序,将排序后的顺序作为该区域度量值的分数:圆形度分数表示为Scirclarity、对称度分数表示为Ssymmetry、横纵比分数表示为Sratio、面积分数表示为Sarea6) Sort the four measurement parameters calculated in step 5): the circularity and area are sorted from large to small, the aspect ratio of the bounding box is sorted from small to large according to the difference from 1, the center Symmetry is sorted from small to large, and the sorted order is used as the score of the measurement value of the area: the circularity score is expressed as S circularity , the symmetry score is expressed as S symmetry , the aspect ratio score is expressed as S ratio , The area fraction is expressed as S area ;

7)定义血管截面相似性分数:Score=w1*Scirclarity+w2*Ssymmetry+w3*Sratio+w4*Sarea,其中,w1、w2、w3、w4分别表示候选血管区域圆形度分数、对称度分数、横纵比分数以及面积分数对应的权值;血管相似性分数越小,表示该区域越可能是血管区域;7) Define the similarity score of blood vessel section: Score=w1*S circlarity +w2*S symmetry +w3*S ratio +w4*S area , where w1, w2, w3, and w4 represent the circularity score of the candidate blood vessel area, Weights corresponding to symmetry score, aspect ratio score, and area score; the smaller the blood vessel similarity score, the more likely the area is to be a blood vessel area;

8)将血管相似性分数Score按照从小到大的顺序进行排序,获取排名前四的候选区域;首先将面积较大的两个定义为左、右颈动脉的起始区域,另外两个定义为左、右椎动脉的起始区域;然后根据得到的颈动脉和椎动脉起始区域重心的x坐标来区分左、右颈动脉和左、右椎动脉;最后将血管区域的重心作为左、右颈动脉起点(如图3A中的A点和B点)和左、右椎动脉的起点(如图3A中的C点和D点)。8) Sort the blood vessel similarity scores in order from small to large, and obtain the top four candidate regions; first, define the two larger areas as the starting areas of the left and right carotid arteries, and the other two are defined as The starting areas of the left and right vertebral arteries; then, the left and right carotid arteries and the left and right vertebral arteries are distinguished according to the obtained x-coordinates of the center of gravity of the starting areas of the carotid and vertebral arteries; finally, the center of gravity of the vascular area is used as the left and right The origin of the carotid artery (points A and B in Figure 3A) and the origin of the left and right vertebral arteries (points C and D in Figure 3A).

9)如果筛选出的区域小于4个,或者筛选出的区域在人体组织中心(x方向)的一侧,则以当前候选层为基准,继续向上扫描,同时返回S1.1.1继续寻找颈动脉和椎动脉的起点。9) If the screened area is less than 4, or the screened area is on the side of the center of the human tissue (x direction), the current candidate layer is used as the benchmark, continue to scan upward, and return to S1.1.1 to continue to search for the carotid artery and Origin of the vertebral artery.

由于椎动脉起于锁骨下动脉,颈动脉起于主动脉弓和锁骨下动脉,椎动脉和颈动脉的起点位于符合要求的CTA影像数据的下半部,因此对于颈动脉和椎动脉起点的定位直到CTA数据的下半部被逐层扫描。Since the vertebral artery originates from the subclavian artery, and the carotid artery originates from the aortic arch and subclavian artery, the origins of the vertebral and carotid arteries are located in the lower half of the eligible CTA image data, so the location of the origin of the carotid and vertebral arteries until CTA The bottom half of the data is scanned layer by layer.

S1.2自动定义椎动脉血管、颈动脉血管的终点S1.2 Automatically define the endpoints of vertebral and carotid vessels

S1.2.1自动定义椎动脉血管的终点(即基底动脉点)S1.2.1 Automatically define the end point of the vertebral artery vessel (ie the basilar artery point)

S1.2.1.1定位基底动脉点候选层面S1.2.1.1 Locate candidate slices of basilar artery point

从上至下逐层(从颅顶至主动脉弓方向)扫描,以120HU灰度值作为阈值提取真实的脑组织,同时计算真实脑组织和S1.1.1中二值化处理后的体组织包围盒;当脑组织的包围盒面积大小为体组织包围盒的1/3时,停止扫描,并将该层作为基底动脉的候选层面。Scan from top to bottom layer by layer (from the top of the skull to the aortic arch), extract the real brain tissue with the gray value of 120HU as the threshold, and calculate the real brain tissue and the body tissue bounding box after binarization in S1.1.1; When the size of the bounding box of the brain tissue is 1/3 of the bounding box of the body tissue, the scanning is stopped, and this layer is taken as the candidate layer of the basilar artery.

S1.2.1.2定位基底动脉点S1.2.1.2 Locate the basilar artery point

在候选层面中对灰度值在150-750HU的区域进行标记提取,获得基底动脉的候选区域,并使用如下规则对候选区域进行筛选:In the candidate slice, the area with the gray value of 150-750HU is marked and extracted to obtain the candidate area of the basilar artery, and the following rules are used to screen the candidate area:

1)计算标记组织的重心,去除重心未被标记的区域;1) Calculate the center of gravity of the marked tissue, and remove the unmarked area of the center of gravity;

2)采用基于Hessian矩阵的血管增强函数,对步骤1)过滤后的候选区域进行增强处理;2) using the blood vessel enhancement function based on the Hessian matrix to perform enhancement processing on the candidate region filtered in step 1);

3)以0.6作为血管增强的阈值,去除小于该阈值对应的候选区域;3) Take 0.6 as the threshold value of blood vessel enhancement, and remove the candidate area corresponding to less than the threshold value;

4)以步骤3)过滤后的候选区域重心为种子点,100HU作为步长,进行二维区域生长;4) Take the center of gravity of the filtered candidate region in step 3) as the seed point and 100HU as the step size, and carry out two-dimensional region growth;

5)根据正常基底动脉的尺寸特征,去除面积小于1mm2,大于35mm2的血管候选区域;5) According to the size characteristics of the normal basilar artery, remove the blood vessel candidate area with an area less than 1mm 2 and greater than 35mm 2 ;

6)计算每一个候选区域的包围盒,去除包围盒横纵比大于4的区域;6) Calculate the bounding box of each candidate area, and remove the area with the aspect ratio of the bounding box greater than 4;

7)提取血管候选区域的边界,计算重心点到边界点的最大、最小距离,去除最大距离与最小距离的比值大于8的血管候选区域;7) extracting the boundary of the blood vessel candidate area, calculating the maximum and minimum distances from the center of gravity point to the boundary point, and removing the blood vessel candidate area where the ratio of the maximum distance to the minimum distance is greater than 8;

8)以血管候选区域重心作为种子点,100HU作为步长,对上、下两层图像进行三维区域生长,提取与当前血管候选区域相邻接的血管区域,并计算邻接血管区域的包围盒;去除包围盒的横纵比大于4,或者最长边大于10mm的血管候选区域;8) Using the center of gravity of the blood vessel candidate area as the seed point and 100HU as the step size, perform three-dimensional region growth on the upper and lower layers of images, extract the blood vessel area adjacent to the current blood vessel candidate area, and calculate the bounding box of the adjacent blood vessel area; Remove the blood vessel candidate regions with the aspect ratio of the bounding box greater than 4, or the longest side greater than 10mm;

9)计算步骤8)筛选后的血管候选区域的居中度、高度以及面积,其中,居中度表示血管候选区域重心相对于脑组织中心的距离;高度表示血管候选区域重心y方向的坐标;9) Calculate the centering degree, height and area of the blood vessel candidate region after the screening in step 8), wherein the centering degree represents the distance of the center of gravity of the blood vessel candidate region relative to the center of the brain tissue; the height represents the coordinate in the y direction of the center of gravity of the blood vessel candidate region;

10)对步骤9)中计算出的3种度量参数进行排序:其中,居中度按照从小到大排序,高度和面积按照从大到小排序,将排序后的顺序作为该区域该种度量值的分数:居中度分数表示为Scenter、高度分数表示为Sy、面积分数表示为Sarea10) Sort the three metric parameters calculated in step 9): among them, the centering degree is sorted from small to large, the height and area are sorted from large to small, and the sorted order is used as the metric value of the area. Score: The centering score is expressed as S center , the height score is expressed as S y , and the area score is expressed as S area ;

11)构建血管相似性分数Score=w1*Scenter+w2*Sy+w3*Sarea,其中w1、w1、w3分别表示候选血管区域居中度分数、高度分数以及面积所对应的权值;将血管相似性分数最小的血管候选区域重心作为基底动脉点,如图3B中的E点;11) Build a blood vessel similarity score Score=w1*S center +w2*S y +w3*S area , where w1, w1, and w3 represent the weights corresponding to the candidate blood vessel area center score, height score, and area respectively; The centroid of the blood vessel candidate region with the smallest blood vessel similarity score is used as the basilar artery point, such as point E in Figure 3B;

12)如果经过步骤8)筛选后不存在血管候选区域,则以当前层为基准,继续向下扫描,同时返回S1.2.1.1继续寻找基地动脉点。12) If there is no blood vessel candidate area after screening in step 8), take the current layer as the benchmark, continue to scan downward, and return to S1.2.1.1 to continue searching for the base arterial point.

对于符合要求的CTA影像数据,基底动脉位点于数据的上半部,因此对于基底动脉点的定位直到CTA数据的上半部被逐层扫描。For the CTA image data that meets the requirements, the basilar artery is located in the upper half of the data, so the location of the basilar artery point is scanned layer by layer until the upper half of the CTA data.

S1.2.2定位颈动脉终点即颈内动脉终点S1.2.2 Locate the end point of the carotid artery, that is, the end point of the internal carotid artery

S1.2.2.1定位颈内动脉终点候选层面S1.2.2.1 Locating the candidate slice for the end point of the internal carotid artery

从上至下(从颅顶至主动脉弓方向)逐层扫描,计算颅骨包围盒,以颅骨包围盒向内收缩1/2的区域构建颅内组织包围盒,统计该包围盒内部灰度值大于550HU的像素点个数;如果包围盒范围内所有像素点均为大于550HU,则判定当前层为颅顶区域,此时不做判断,继续向下扫描;当包围盒内所有像素的灰度值均小于550HU,此时认为扫描已经进入到脑组织区域;当出现脑组织区域后,逐层判断包围盒中骨像素点的个数与其所在组织包围盒所有像素点个数的比例,如果该比例大于0.005时,停止搜索,并将该层作为颈内动脉终点的候选层面。Scan layer by layer from top to bottom (from the top of the skull to the direction of the aortic arch), calculate the skull bounding box, construct the intracranial tissue bounding box with the area where the skull bounding box shrinks inward by 1/2, and calculate that the gray value inside the bounding box is greater than 550HU If all the pixels in the bounding box are larger than 550HU, the current layer is determined to be the cranial top area, no judgment is made at this time, and the scan continues downward; when the gray values of all the pixels in the bounding box are equal to Less than 550HU, it is considered that the scan has entered the brain tissue area; when the brain tissue area appears, the ratio of the number of bone pixels in the bounding box to the number of all pixels in the tissue bounding box is determined layer by layer, if the ratio is greater than At 0.005, the search was stopped and this layer was taken as a candidate layer for the end point of the internal carotid artery.

S1.2.2.2定位颈动脉终点S1.2.2.2 Locate the carotid end point

1)计算S1.1中定位出的颈动脉起点所在的血管区域的灰度均值CTmean和标准差CTstd,以颈动脉起点作为种子点,以CTmean+CTstd、CTmean-CTstd作为上下阈值,进行单向向上的三维区域生长,直到颈内动脉终点的候选层面,同时标记在候选层面中的候选血管区域;1) Calculate the gray mean CT mean and standard deviation CT std of the blood vessel area where the carotid artery origin is located in S1.1, take the carotid artery origin as the seed point, and take CT mean + CT std and CT mean - CT std as the The upper and lower thresholds are used for unidirectional upward three-dimensional region growth until the candidate level of the end point of the internal carotid artery, and the candidate blood vessel region in the candidate level is marked at the same time;

2)在颈内动脉终点所在的层面中,寻找灰度值高于750HU的像素点,并将其作为骨组织种子点,通过二维区域生长的方式提取阈值在120-3071HU范围内所有与该种子点联通的组织,去除步骤1)中与骨粘连的候选血管区域;2) In the layer where the end point of the internal carotid artery is located, look for the pixel points whose gray value is higher than 750HU, and use it as the seed point of bone tissue, and extract all the threshold values within the range of 120-3071HU that are related to the For the tissue connected by the seed point, remove the candidate blood vessel area that is adhered to the bone in step 1);

3)计算步骤2)筛选后得到的血管候选区域的中心对称度Symmetry、包围盒横纵比Ratio以及区域面积Area,其中,中心对称度Symmetry的定义与S1.1.2中步骤5)的定义相同;3) Calculate the central symmetry degree Symmetry, the bounding box aspect ratio Ratio and the area area of the blood vessel candidate region obtained after the screening in step 2), wherein the definition of the central symmetry degree Symmetry is the same as the definition in step 5) in S1.1.2;

4)对步骤3)中计算出的3种度量参数进行排序:其中,中心对称度按照从小到大排序,包围盒横纵比按照与1的差值从小到大进行排序,区域面积按照从大到小进行排序。将排序后的顺序作为该区域度量值的分数:中心对称度分数表示为Ssymmetry、包围盒横纵比分数表示为Sratio、区域面积分数表示为Sarea4) Sort the three measurement parameters calculated in step 3): among them, the central symmetry is sorted from small to large, the aspect ratio of the bounding box is sorted from small to large according to the difference from 1, and the area area is sorted from large to large. Sort by small. Take the sorted order as the score of the metric value of the area: the center symmetry score is expressed as S symmetry , the horizontal and vertical ratio score of the bounding box is expressed as S ratio , and the area area score is expressed as S area ;

5)定义血管截面相似性分数:Score=w1*Ssymmetry+w2*Sratio+w3*Sarea。其中,w1、w2、w3分别表示候选血管区域中心对称度分数、包围盒横纵比分数以及区域面积分数对应的权值。血管相似性分数越小,表示该区域越可能是血管区域;5) Define the similarity score of blood vessel section: Score=w1*S symmetry +w2*S ratio +w3*S area . Among them, w1, w2, and w3 represent the corresponding weights of the candidate blood vessel region center symmetry score, the bounding box aspect ratio score, and the region area score, respectively. The smaller the blood vessel similarity score, the more likely the area is to be a blood vessel area;

6)将血管截面相似性分数Score按照从小到大的顺序进行排序,将分数最高的两个血管候选区域作为所对应的重心依据x坐标方向定义为左、右颈动脉的终点,如图3C中的F点和G点;6) Sort the blood vessel cross-section similarity score in the order from small to large, and take the two blood vessel candidate regions with the highest scores as the corresponding center of gravity and define the end points of the left and right carotid arteries according to the x-coordinate direction, as shown in Figure 3C F point and G point of ;

7)如果经过步骤2)筛选后的候选血管区域小于2个,则以当前层为基准,继续向下扫描,同时返回步骤2)继续寻找劲动脉终点。7) If there are less than 2 candidate blood vessel regions after screening in step 2), take the current layer as the benchmark, continue to scan downward, and return to step 2) to continue searching for the end point of the carotid artery.

对于符合要求的CTA影像数据,颈动脉终点位于数据的上半部,因此对于颈内动脉点的定位直到CTA数据的上半部被逐层扫描。。For the CTA image data that meets the requirements, the end point of the carotid artery is located in the upper half of the data, so for the positioning of the internal carotid artery point until the upper half of the CTA data is scanned slice by slice. .

本发明步骤S2:在步骤S1定位的血管起点和终点之间,对符合血管灰度特征的区域进行基于Raycasting的多尺度梯度归一化血管滤波,构建血管滤波权值图。Step S2 of the present invention: between the start point and the end point of the blood vessel located in step S1, perform multi-scale gradient normalized blood vessel filtering based on Raycasting on the region conforming to the grayscale characteristics of the blood vessel, and construct a blood vessel filtering weight map.

S2.1血管预提取S2.1 Vascular Pre-Extraction

依据S1.1中定位的颈动脉和椎动脉血管区域,分别计算对应区域的灰度均值CTmean和标准差CTstd,以S1.1中定位的颈动脉和椎动脉起点作为种子点,以CTmean+2*CTstd、CTmean-2*CTstd作为上下阈值,进行单向向上的三维区域生长;颈动脉生长至颈动脉终点所在层面为止,椎动脉生长至椎动脉终点所在层面为止;According to the carotid artery and vertebral artery vascular area located in S1.1, the gray mean CT mean and standard deviation CT std of the corresponding area were calculated respectively, and the starting point of the carotid artery and vertebral artery located in S1. mean +2*CT std and CT mean -2*CT std are used as the upper and lower thresholds to perform unidirectional upward three-dimensional regional growth; the carotid artery grows to the level where the end point of the carotid artery is located, and the vertebral artery grows to the level where the end point of the vertebral artery is located;

S2.2基于Raycasting的多尺度梯度归一化血管滤波S2.2 Raycasting-based Multiscale Gradient Normalized Vascular Filtering

在CTA图像中,血管截面具有类圆形的形状特征,灰度变化具有类高斯分布的特征(血管中心的灰度值较高,以中心为圆心随着半径的增大,灰度值逐渐降低)。本发明所述的基于Raycasting的多尺度梯度归一化血管滤波正是利用血管的这两个特征,计算出S2.1中预提取血管区域每一个像素点的血管滤波值,具体步骤如下:In the CTA image, the cross-section of the blood vessel has the characteristics of a circle-like shape, and the grayscale change has the characteristics of a Gaussian-like distribution (the gray value of the center of the blood vessel is higher, and the gray value gradually decreases with the increase of the radius with the center as the center. ). The multi-scale gradient normalized blood vessel filtering based on Raycasting of the present invention utilizes these two characteristics of blood vessels to calculate the blood vessel filtering value of each pixel point in the pre-extracted blood vessel area in S2.1, and the specific steps are as follows:

1)由于血管截面尺寸在其走形中会发生变化,所以需要在不同半径尺度下计算血管的滤波值(如图2所示),R∈(Rmin,Rmax](后续步骤中以Rmin和Rmax表示最小和最大半径尺度,不再区分颈动脉和椎动脉);1) Since the cross-sectional size of the blood vessel will change in its course, it is necessary to calculate the filtering value of the blood vessel under different radius scales (as shown in Figure 2), R ∈ (R min , R max ] (in the following steps, R min and Rmax represent minimum and maximum radius scales, no longer distinguish between carotid and vertebral arteries);

2)以S2.1中预提取的任一血管区域点为圆心,分别计算投射半径尺度为R,圆心点的梯度响应VR,0到R半径尺度下梯度响应的最小值VR,min,Rmin到Rmax半径尺度下的最大值VR,max2) Taking any blood vessel region point pre-extracted in S2.1 as the center of the circle, calculate the projection radius scale as R, the gradient response VR of the center point, and the minimum value VR ,min of the gradient response under the radius scale from 0 to R , The maximum value VR ,max under the radius scale from R min to R max ;

3)定义半径尺度R下的归一化梯度响应为ER=(VR-VR,min)/VR,max,如果当前点为血管中心点,ER约等于1;如果为非血管点,ER约等于0;3) Define the normalized gradient response under the radius scale R as E R =(VR -VR ,min )/VR ,max , if the current point is the center point of the blood vessel, E R is approximately equal to 1; if it is a non-vessel point point, E R is approximately equal to 0;

4)重复步骤2)-3),计算多个投射半径尺度Rmin到Rmax下的ER,并将不同尺度下ER的最大值作为该点最终的血管滤波值;4) Repeat steps 2)-3), calculate ER under multiple projection radius scales R min to R max , and use the maximum value of ER under different scales as the final blood vessel filter value at this point;

S2.3构建血管滤波权值图S2.3 Construct blood vessel filtering weight map

对于每一个S2.1中提取的血管点,通过S2.2都可以计算出一个血管的滤波值,定义血管点的血管滤波权值为:For each blood vessel point extracted in S2.1, the filtering value of a blood vessel can be calculated through S2.2, and the blood vessel filtering weight of the blood vessel point is defined as:

其中,分别为相邻两点在S2.2中计算出的血管滤波值倒数,Dist为两点之间的物理距离。in, and are the reciprocal of the blood vessel filter values calculated in S2.2 for two adjacent points, respectively, and Dist is the physical distance between the two points.

根据计算出的所有血管点的血管滤波权值,构建出血管滤波值权值图。According to the calculated blood vessel filter weights of all blood vessel points, a blood vessel filter value weight map is constructed.

本发明步骤S3:在步骤S2构建的血管滤波权值图中,采用Dijkstra最优路径提取算法完成对颈动脉和椎动脉血管中心路径的提取。Step S3 of the present invention: In the blood 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 the vertebral artery.

采用Dijkstra的最优路径提取算法获取血管起始点和终止点之间的最短中心路径,首先,定义步骤S1中定位的血管起点和终点之间的能量函数:Dijkstra's optimal path extraction algorithm is used to obtain the shortest center path between the starting point and the ending point of the blood vessel. First, define the energy function between the starting point and the ending point of the blood vessel located in step S1:

E=∫(w(s)+ε)dsE=∫(w(s)+ε)ds

其中,w(s)为S2.3中计算出的血管滤波增强权值,ε为正则项,s为起点和终点之间的路径。Among them, w(s) is the blood vessel filter enhancement weight calculated in S2.3, ε is the regular term, and s is the path between the starting point and the ending point.

然后,选取血管起点和终点之间所有路径中血管滤波权值和最小的路径即为血管中心路径。Then, the path with the smallest sum of the blood vessel filter weights among all the paths between the starting point and the end point of the blood vessel is selected as the center path of the blood vessel.

图3D为提取的右侧颈动脉中心路径及其对应的CPR(曲面重建)结果图,图3E为提取的左侧颈动脉中心路径及其对应的CPR(曲面重建)结果图,图3F为提取的右侧椎动脉中心路径及其对应的CPR(曲面重建)结果图,图3G为提取的左侧椎动脉中心路径及其对应的CPR(曲面重建)结果图。Figure 3D is the extracted right carotid artery center path and its corresponding CPR (surface reconstruction) result, Figure 3E is the extracted left carotid artery center path and its corresponding CPR (surface reconstruction) result, and Figure 3F is the extracted The central path of the right vertebral artery and its corresponding CPR (curved surface reconstruction) result diagram, Figure 3G is the extracted left vertebral artery central path and its corresponding CPR (curved surface reconstruction) result diagram.

本发明与现有技术相比,具有的优点:Compared with the prior art, the present invention has the following advantages:

1、实现血管的自动定位,无需用户手动参与;1. Realize the automatic positioning of blood vessels, without the user's manual participation;

2、提取的中心线准确,无需用户后期调整;2. The extracted centerline is accurate, and no user adjustment is required later;

3、方法运行速度快,满足实时性需求。3. The method runs fast and meets real-time requirements.

最后应说明的是:以上所述的各实施例仅用于说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述实施例所记载的技术方案进行修改,或者对其中部分或全部技术特征进行等同替换;而这些修改或替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的范围。Finally, it should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that : it can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements to some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the embodiments of the present invention range.

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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CN114972401B (en) * 2022-06-08 2025-07-01 南京钺曦医疗科技有限公司 An image processing method for separating head and neck
CN115049677A (en) * 2022-06-10 2022-09-13 杭州脉流科技有限公司 CTA image-based intracranial blood vessel center path extraction method and device
CN118735912A (en) * 2024-08-30 2024-10-01 广州医科大学附属第一医院(广州呼吸中心) A spinal deformity identification method for auxiliary diagnosis of abdominal aortic aneurysm
CN118735912B (en) * 2024-08-30 2024-11-19 广州医科大学附属第一医院(广州呼吸中心) Spinal column deformation identification method for auxiliary diagnosis of abdominal aortic aneurysm

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