An efficient level set method based on multi-scale image segmentation and hermite differential operator
文献类型：期刊论文
| 作者 | Wang, Xiao-Feng1,2; Min, Hai2,3; Zou, Le1; Zhang, Yi-Gang1; Tang, Yuan-Yan4; Chen, Chun-Lung Philip4 |
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2016-05-05 |
| 卷号 | 188期号:无页码:90-101 |
| 关键词 | Hermite Differential Operator Image Segmentation Intensity Inhomogeneity Level Set Multi-scale |
| DOI | 10.1016/j.neucom.2014.10.112 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| WOS关键词 | PROBABILISTIC NEURAL-NETWORKS ; GEODESIC ACTIVE CONTOURS ; INTENSITY INHOMOGENEITIES ; FACE RECOGNITION ; CURVE EVOLUTION ; FITTING ENERGY ; SHAH MODEL ; MUMFORD ; MRI ; ALGORITHM |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000375170000011 |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/22386]  |
| 专题 | 合肥物质科学研究院_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. |
入库方式： OAI收割
来源：合肥物质科学研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。