A robust level set method based on local statistical information for noisy image segmentation |
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| Affiliation: | 1. Graduate School of Science and Technology, Kumamoto University, Kurokami 2-39-1, Kumamoto 860-8555, Japan;2. PHOENICS, 3-11-38 Higashimachi, Higashi-ku, Kumamoto 862-0901, Japan;3. JST-CREST, 5 Sanbancho, Chiyoda-ku, Tokyo 102-0075, Japan;4. Innovate Collaboration Organization, Kumamoto University, Kurokami 2-39-1, Kumamoto 860-8555, Japan;1. Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, Feixi Road 3#, Hefei 230039, China;2. Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China |
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| Abstract: | The paper presents an improved local region-based active contour model for image segmentation, which is robust to noise. A data fitting energy functional is defined in terms of curves and the energy terms which are based on the differences between the local average intensity and the global intensity means. Then the energy is incorporated into a level set variational formulation, from which a curve evolution equation is derived for energy minimization. And then the level set function is regularized by Gaussian filter to keep smooth and eliminate the re-initialization. By using the local statistical information, the proposed model can handle with noisy images. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase one. Experimental results show desirable performances of the proposed model for both noisy synthetic and real images, especially with high level noise. |
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| Keywords: | Active contour model Image segmentation Level set method Statistical information Noisy image |
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