Fuzzy c-means clustering with bilateral filtering for medical image segmentation

Authors:
Yuchen Liu;Kai Xiao;Alei Liang;Haibing Guan
Affiliations:
Shanghai Key Laboratory of Scalable Computing and Systems, School of Software, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems, School of Software, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems, School of Software, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems, School of Software, Shanghai Jiao Tong University, Shanghai, China
Venue:
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Year:
2012

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Abstract

Fuzzy c-means (FCM) is a widely used unsupervised pattern recognition method for medical image segmentation. The conventional FCM algorithm and some existing variants are either sensitive to noise or prone to loss of details. This paper presents a modified FCM algorithm that incorporates bilateral filtering for medical image segmentation. The experimental results and quantitative analyses suggest that, compared to the conventional FCM, the proposed method improves clustering performance with higher standard of noise-resistance and detail-preservation.