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An efficient level set method based on multi-scale image segmentation and hermite differential operator
Xiao-Feng Wang1,2; Hai Min2,3; Le Zou1; Yi-Gang Zhang1; Yuan-Yan Tang4; Chun-Lung Philip Chen4
Source PublicationNeurocomputing

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.

KeywordHermite Differential Operator Image Segmentation Intensity Inhomogeneity Level Set Multi-scale
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000375170000011
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Cited Times [WOS]:10   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Corresponding AuthorXiao-Feng Wang; Hai Min; Le Zou; Yi-Gang Zhang; Yuan-Yan Tang; Chun-Lung Philip Chen
Affiliation1.Key Lab of Network and Intelligent Information Processing, Department of Computer Science and Technology, Hefei University, Hefei, Anhui 230601, China
2.Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China
3.Department of Automation, University of Science and Technology of China, Hefei, Anhui 230027, China
4.Faculty of Science and Technology, University of Macau, Macau, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Xiao-Feng Wang,Hai Min,Le Zou,et al. An efficient level set method based on multi-scale image segmentation and hermite differential operator[J]. Neurocomputing,2016,188:90-101.
APA Xiao-Feng Wang,Hai Min,Le Zou,Yi-Gang Zhang,Yuan-Yan Tang,&Chun-Lung Philip Chen.(2016).An efficient level set method based on multi-scale image segmentation and hermite differential operator.Neurocomputing,188,90-101.
MLA Xiao-Feng Wang,et al."An efficient level set method based on multi-scale image segmentation and hermite differential operator".Neurocomputing 188(2016):90-101.
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