Department of Digital Media Technology, Dalian University of Technology, Dalian 116620, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian 116620, China.
Department of Digital Media Technology, Dalian University of Technology, Dalian 116620, China.
Comput Methods Programs Biomed. 2018 Jul;161:55-72. doi: 10.1016/j.cmpb.2018.04.010. Epub 2018 Apr 18.
Cerebrovascular pathology is one of the main fatal diseases which seriously affect the human's health. Extracting the accurate image of cerebral vascular tissue is the key of clinical diagnosis. However, the motion artifacts in DSA images seriously affected the quality of vascular subtraction image. In this paper, an automatic and accurate segmentation method is presented to extract the vascular region in the live image of brain. Firstly, a coarse registration for the live image and the mask image is implemented. And then, the SIFT algorithm is utilized to detect geometrical feature points in the serialized subtraction images. After that, a spatial model of rotating coordinate system and a calculative strategy of contextual information are designed to eliminate the error feature points. Finally, based on a dynamic threshold method, the blood vessel image can be obtained by region growing. The context information in the adjacent subtraction images is fully used. The experimental result shows that the segmented cerebral vascular image is satisfactory. This method can provide accurate vessel image data for the clinical operation based on DSA interventional therapy.
脑血管病理学是严重影响人类健康的主要致命疾病之一。准确提取脑血管组织的图像是临床诊断的关键。然而，DSA 图像中的运动伪影严重影响了血管减影图像的质量。本文提出了一种自动、准确的分割方法，用于从脑部实时图像中提取血管区域。首先，对实时图像和蒙版图像进行粗略配准。然后，利用 SIFT 算法在序列化减影图像中检测几何特征点。之后，设计了旋转坐标系的空间模型和上下文信息的计算策略来消除错误的特征点。最后，基于动态阈值方法，通过区域生长得到血管图像。充分利用了相邻减影图像中的上下文信息。实验结果表明，分割后的脑血管图像令人满意。该方法可以为基于 DSA 介入治疗的临床手术提供准确的血管图像数据。
