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

doi:10.1109/TNB.2020.2996604

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	A Parallel Cerebrovascular Segmentation Algorithm Based on Focused Multi-Gaussians Model and Heterogeneous Markov Random Field
	Lv, Zhilong 2,3; Mi, Fubo 3,4; Wu, Zhongke 1; Zhu, Yicheng 5; Liu, Xinyu 2,3; Tian, Mei 4; Zhang, Fa 2,3; Wang, Xingce 1; Wan, Xiaohua 2,3
刊名	IEEE TRANSACTIONS ON NANOBIOSCIENCE
	2020-07-01
卷号	19 期号:3 页码:538-546
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
ISSN号	1536-1241
DOI	10.1109/TNB.2020.2996604
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000545423500023
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/15101]
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.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing, Peoples R China 4.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, 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.