Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation

  • Authors:

  • Wen-Liang Hung;Miin-Shen Yang;De-Hua Chen

  • Affiliations:

  • Department of Applied Mathematics, National Hsinchu University of Education, Hsin-Chu, Taiwan, ROC;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan, ROC;Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan, ROC

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2006

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Abstract

This paper presents an algorithm, called the modified suppressed fuzzy c-means (MS-FCM), that simultaneously performs clustering and parameter selection for the suppressed fuzzy c-means (S-FCM) algorithm proposed by [Fan, J.L., Zhen, W.Z., Xie, W.X., 2003. Suppressed fuzzy c-means clustering algorithm. Pattern Recognition Lett. 24, 1607-1612]. The proposed algorithm is computationally simple, and is able to select the parameter @a in S-FCM with a prototype-driven learning. The parameter selection is based on the exponential separation strength between clusters. Numerical examples will serve to illustrate the effectiveness of the proposed MS-FCM algorithm. Finally, the S-FCM and MS-FCM algorithms are applied in the segmentation of the magnetic resonance image (MRI) of an ophthalmic patient. In our comparisons of S-FCM, MS-FCM, alternative FCM (AFCM) proposed by [Wu, K.L., Yang, M.S., 2002. Alternative c-means clustering algorithms. Pattern Recognition 35, 2267-2278] and similarity-based clustering method (SCM) proposed by [Yang, M.S., Wu, K.L., 2004. A similarity-based robust clustering method. IEEE Trans. Pattern Anal. Machine Intell. 26, 434-448] for these MRI segmentation results, we find that these four techniques provide useful information as an aid to diagnosis in ophthalmology. However, the MS-FCM provides better detection of abnormal tissue than S-FCM, AFCM and SCM when based on a window selection. Overall, the MS-FCM clustering algorithm is more efficient and is strongly recommended as an MRI segmentation technique.