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A Cerebrovascular Image Segmentation Method Based on Geometrical Feature Point Clustering and Local Threshold

文献类型：期刊论文

作者Liu, Bin5,6; Zhu, Chen4; Qu, Xiaofeng2; Wang, Mingzhe6; Zhang, Song6; Wang, Yi5,6; Fan, Xin5,6; Luo, Zhongxuan5,6; Zhang, Bingbing3; Yue, Zongge1
刊名CURRENT MEDICAL IMAGING REVIEWS
出版日期2018
卷号14期号:5页码:748-770
关键词Digital subtraction angiography cerebrovascular image segmentation feature point clustering local threshold SIFT algorithm DSA interventional therapy
ISSN号1573-4056
DOI10.2174/1573405613999170922143513
英文摘要Background: For the cerebrovascular Digital Subtraction Angiography (DSA), how to restrain the patient motion artifact to improve the quality of subtraction image has an important effect on the clinical diagnosis. Methods: Currently, image registration is the main way to extract the blood vessels. However, there is usually massive calculation in the registration process. And it is usually only suitable for simple rigid motion artifact. Instead of registration way, a novel cerebrovascular segmentation method was proposed to extract blood vessels in this paper. In this method, the geometrical feature points of mask image and live image were firstly detected by SIFT algorithm under same restrain parameters. Secondly, the feature points were clustered and the subtraction of clustered point set was implemented. Then, the coordinates of the residual feature points were adjusted based on gray gradient. Lastly, the vessel image was segmented based on region growing and local threshold. Result: Experiments for the sequential cerebrovascular DSA images illustrate the applicability of this method. The quality of the vessel image after segmentation was satisfactory. The interdependency of geometrical feature information for both mask image and live image was adequately utilized in this new method. Conclusion: This method can provide accurate vessel image data for the clinical operation based on DSA interventional therapy.
资助项目National Natural Science Foundation of China[61300085] ; National Natural Science Foundation of China[61572101] ; Scientific Research Fund of Liaoning Provincial Education Department of China[L2013012] ; Fundamental Research Funds for the Central Universities of China[DUT14QY18]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000443684600011
出版者BENTHAM SCIENCE PUBL LTD
源URL[http://119.78.100.204/handle/2XEOYT63/4982]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Bingbing
作者单位1.Dalian Univ Technol, Affiliated Hosp, Dalian, Peoples R China
2.Dalian Med Univ, Hosp 2, Dalian, Peoples R China
3.Dalian Med Univ, Modern Technol & Educ Dept, Dalian, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
5.Dalian Univ Technol, Key Lab Ubiquitous Network & Serv Software Liaoni, Dalian, Peoples R China
6.Dalian Univ Technol, Int Sch Informat Sci & Engn DUT RUISE, Dalian, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Bin,Zhu, Chen,Qu, Xiaofeng,et al. A Cerebrovascular Image Segmentation Method Based on Geometrical Feature Point Clustering and Local Threshold[J]. CURRENT MEDICAL IMAGING REVIEWS,2018,14(5):748-770.
APA Liu, Bin.,Zhu, Chen.,Qu, Xiaofeng.,Wang, Mingzhe.,Zhang, Song.,...&Yue, Zongge.(2018).A Cerebrovascular Image Segmentation Method Based on Geometrical Feature Point Clustering and Local Threshold.CURRENT MEDICAL IMAGING REVIEWS,14(5),748-770.
MLA Liu, Bin,et al."A Cerebrovascular Image Segmentation Method Based on Geometrical Feature Point Clustering and Local Threshold".CURRENT MEDICAL IMAGING REVIEWS 14.5(2018):748-770.

入库方式： OAI收割

来源：计算技术研究所

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