A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field

doi:10.1109/TNB.2020.2996604

	A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field
	Lv, Zhilong 1,2; Mi, Fubo 1,3; Wu, Zhongke 4; Zhu, Yicheng 5; Liu, Xinyu 1,2; Tian, Mei 3; Zhang, Fa 1,2; Wang, Xingce 4; Wan, Xiaohua 1,2
	2020-07-01
发表期刊	IEEE TRANSACTIONS ON NANOBIOSCIENCE
ISSN	1536-1241
卷号	19 期号:3 页码:538-546
摘要	A complete and detailed cerebrovascular image segmented from time-of-flight magnetic resonance angiography (TOF-MRA) data is essential for the diagnosis and therapy of the cerebrovascular diseases. In recent years, three-dimensional cerebrovascular segmentation algorithms based on statistical models have been widely used, but the existed methods always perform poorly on stenotic vessels and are not robust enough. In this paper, we propose a parallel cerebrovascular segmentation algorithm based on focused multi-Gaussians model and heterogeneous Markov random field. Specifically, we present a focused multi-Gaussians (FMG) model with local fitting region to model the vascular tissue more accurately and introduce the chaotic oscillation particle swarm optimization (CO-PSO) algorithm to improve the global optimization capability in the parameter estimation. Furthermore, we design a heterogeneous Markov Random Field (MRF) in the three-dimensional neighborhood system to incorporate precise local character of image. Finally, the algorithm has been performed parallel optimization based on GPUs and obtain about 60 times speedup compared to serial execution. The experiments show that the proposed algorithm can produce more detailed segmentation result in shorter time and performs well on the stenotic vessels robustly.
关键词	Gaussian distribution Image segmentation Particle swarm optimization Optimization Histograms Markov random fields Robustness Three-dimensional cerebrovascular segmentation focused multi-Gaussians model Chaotic oscillation particle swarm optimization Markov random field
DOI	10.1109/TNB.2020.2996604
收录类别	SCI
语种	英语
资助项目	BRICS of China[2017YFE0100500] ; NSFC Project[U1611263] ; NSFC Project[61932018] ; NSFC Project[61672493] ; NSFC Project[61972041] ; National Key Research and Development Program of China[2017YFB1002604] ; National Key Research and Development Program of China[2017YFB1402105] ; National Key Research and Development Program of China[2017YFB1002804] ; Beijing Natural Science Foundation of China[4172033] ; Beijing Natural Science Foundation of China[L182053]
WOS研究方向	Biochemistry & Molecular Biology ; Science & Technology - Other Topics
WOS类目	Biochemical Research Methods ; Nanoscience & Nanotechnology
WOS记录号	WOS:000545423500023
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15102
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, Fa; Wang, Xingce; Wan, Xiaohua
作者单位	1.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China 4.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China 5.Peking Union Med Coll Hosp, Dept Neurol, Beijing 100730, Peoples R China
推荐引用方式 GB/T 7714	Lv, Zhilong,Mi, Fubo,Wu, Zhongke,et al. A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field[J]. IEEE TRANSACTIONS ON NANOBIOSCIENCE,2020,19(3):538-546.
APA	Lv, Zhilong.,Mi, Fubo.,Wu, Zhongke.,Zhu, Yicheng.,Liu, Xinyu.,...&Wan, Xiaohua.(2020).A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field.IEEE TRANSACTIONS ON NANOBIOSCIENCE,19(3),538-546.
MLA	Lv, Zhilong,et al."A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field".IEEE TRANSACTIONS ON NANOBIOSCIENCE 19.3(2020):538-546.