An efficient level set method based on multi-scale image segmentation and hermite differential operator

doi:10.1016/j.neucom.2014.10.112

	An efficient level set method based on multi-scale image segmentation and hermite differential operator
	Wang, Xiao-Feng 1,2; Min, Hai 2,3; Zou, Le 1; Zhang, Yi-Gang 1; Tang, Yuan-Yan 4; Chen, Chun-Lung Philip 4
	2016-05-05
发表期刊	NEUROCOMPUTING
摘要	In this paper, an efficient and robust level set method is presented to segment the images with intensity inhomogeneity. The multi-scale segmentation idea is incorporated into energy functional construction and a new Hermite differential operator is designed to numerically solve the level set evolution equation. Firstly, the circular shape window is used to define local region so as to approximate the image as well as intensity inhomogeneity. Then, multi-scale statistical analysis is performed on intensities of local circular regions centered in each pixel. So, the multi-scale local energy term can be constructed by fitting multi scale approximation of inhomogeneity-free image in a piecewise constant way. To avoid the time-consuming re-initialization procedure, a new double-well potential function is adopted to construct the penalty energy term. Finally, the multi-scale segmentation is performed by minimizing the total energy functional. Here, a new differential operator based on Hermite polynomial interpolation is proposed to solve the minimization. The experiments and comparisons with three popular local region-based methods on images with different levels of intensity inhomogeneity have demonstrated the efficiency and robustness of the proposed method. (C) 2015 Elsevier B.V. All rights reserved.
文章类型	Article
关键词	Hermite Differential Operator Image Segmentation Intensity Inhomogeneity Level Set Multi-scale
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2014.10.112
关键词[WOS]	PROBABILISTIC NEURAL-NETWORKS ; GEODESIC ACTIVE CONTOURS ; INTENSITY INHOMOGENEITIES ; FACE RECOGNITION ; CURVE EVOLUTION ; FITTING ENERGY ; SHAH MODEL ; MUMFORD ; MRI ; ALGORITHM
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000375170000011
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/22386
专题	中科院合肥智能机械研究所
作者单位	1.Hefei Univ, Dept Comp Sci & Technol, Key Lab Network & Intelligent Informat Processing, Hefei 230601, Anhui, Peoples R China 2.Chinese Acad Sci, Hefei Inst Intelligent Machines, Intelligent Comp Lab, POB 1130, Hefei 230031, Anhui, Peoples R China 3.Univ Sci & Technol China, Dept Automat, Hefei 230027, Anhui, Peoples R China 4.Univ Macau, Fac Sci & Technol, Macau, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xiao-Feng,Min, Hai,Zou, Le,et al. An efficient level set method based on multi-scale image segmentation and hermite differential operator[J]. NEUROCOMPUTING,2016,188(无):90-101.
APA	Wang, Xiao-Feng,Min, Hai,Zou, Le,Zhang, Yi-Gang,Tang, Yuan-Yan,&Chen, Chun-Lung Philip.(2016).An efficient level set method based on multi-scale image segmentation and hermite differential operator.NEUROCOMPUTING,188(无),90-101.
MLA	Wang, Xiao-Feng,et al."An efficient level set method based on multi-scale image segmentation and hermite differential operator".NEUROCOMPUTING 188.无(2016):90-101.

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