Abstract
A novel multiresolution approach is presented to segment Brain MRI images using fuzzy clustering. This approach is based on the fact that the image segmentation results should be optimized simultaneously in different scales. A new fuzzy inter-scale constraint based on antistrophic diffusion linkage model is introduced, which builds an efficient linkage relationship between the high resolution images and low resolution ones. Meanwhile, this paper develops two new fuzzy distances and then embeds them into the fuzzy clustering algorithm. The distances describe the fuzzy similarity in adjacent scales effectively. Moreover, a new multiresolution framework combining the inter- and intra-scale constraints is presented. The proposed framework is robust to noise images and low contrast ones, such as medical images. Segmentation of a number of images is illustrated. The experiments show that the proposed approach can extract the objects accurately.
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Yu, G., Wang, C., Zhang, H., Yang, Y., Bian, Z. (2006). A Novel Fuzzy Segmentation Approach for Brain MRI. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2006. Lecture Notes in Computer Science, vol 4179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11864349_81
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DOI: https://doi.org/10.1007/11864349_81
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