An Improved FCM Medical Image Segmentation Algorithm Based on MMTD | Zendy

Ningning Zhou | Zendy; Tingting Yang | Zendy; Shaobai Zhang | Zendy

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An Improved FCM Medical Image Segmentation Algorithm Based on MMTD

Author(s) -

Ningning Zhou,

Tingting Yang,

Shaobai Zhang

Publication year - 2014

Publication title -

computational and mathematical methods in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.462

H-Index - 48

eISSN - 1748-6718

pISSN - 1748-670X

DOI - 10.1155/2014/690349

Subject(s) - image segmentation , segmentation based object categorization , scale space segmentation , artificial intelligence , segmentation , pattern recognition (psychology) , computer science , minimum spanning tree based segmentation , cluster analysis , region growing , fuzzy logic , image (mathematics) , similarity (geometry) , pixel , noise (video) , computer vision , image processing , algorithm

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) is one of the popular clustering algorithms for medical image segmentation. But FCM is highly vulnerable to noise due to not considering the spatial information in image segmentation. This paper introduces medium mathematics system which is employed to process fuzzy information for image segmentation. It establishes the medium similarity measure based on the measure of medium truth degree (MMTD) and uses the correlation of the pixel and its neighbors to define the medium membership function. An improved FCM medical image segmentation algorithm based on MMTD which takes some spatial features into account is proposed in this paper. The experimental results show that the proposed algorithm is more antinoise than the standard FCM, with more certainty and less fuzziness. This will lead to its practicable and effective applications in medical image segmentation.

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