Image Segmentation and Selective Smoothing Based on Variational Framework
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This paper addresses the segmentation and smoothing problems in biomedical imaging under variational framework. In order to get better results, this paper proposes a new segmentation and selective smoothing algorithm. This paper has the following three contributions. First, a new statistical active contour model (SACM) is introduced for noisy image segmentation. SACM is proposed to solve the problem in fast edge integration (FEI) method, which takes advantages of both edge-based and region-based active contour model but only considers the mean information inside and outside of the evolution curve. In SACM, a new statistical term for considering the probability distribution density of regions and a unified variational framework are proposed for construction of different segmentation models with different probability density functions. Moreover, a penalized term is also introduced in the proposed model as internal energy in order to avoid the time consuming re-initialization process. Second, a new symmetric fourth-order PDE denoising algorithm is developed to avoid the blocky effects in second-order PDE model, while preserving edges. Third, in each stage of segmentation process, different denoising algorithms (or different parameters in the same denoising model) can be employed for different sub-regions independently, so that better segmentation and smoothing results can be obtained. Compared with existing methods, our method is more flexible, robust to noise, computationally efficient and produces better results.
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- Image Segmentation and Selective Smoothing Based on Variational Framework
Journal of Signal Processing Systems
Volume 54, Issue 1-3 , pp 145-158
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Image segmentation
- Image smoothing
- Variational framework
- Active contour model
- Fourth-order PDE model
- Median filtering
- Industry Sectors
- Author Affiliations
- 1. College of Mathematics and Computational Science, Shenzhen University, Shenzhen, China
- 2. Department of Computer Science, Hong Kong Baptist University, Hong Kong, Hong Kong
- 3. School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China
- 4. Guangdong Province Key Laboratory of Information Security’, Guangzhou, China
- 5. Key Laboratory of Mathematics Mechanization, CAS, Beijing, China