Enhanced Fuzzy-Based Models for ROI Extraction in Medical Images

Standard Fuzzy C-Means (FCM) clustering has been widely used as an effective method for image segmentation. However, FCM is sensitive to initialization and is easily trapped in local optima. In this paper, several enhanced models for FCM clustering were proposed, namely W_SS_FCM, LAWS_SS_FCM and H_FCM, to promote the performance of standard FCM. The proposed algorithms merge partial supervision with spatial locality to increase conventional FCM’s robustness. A comparison study was conducted to validate the proposed methods’ performance applying well established measures on three datasets. Experimental results show considerable improvement over standard FCM and other variants of the algorithm. It also manifests high robustness against noise attacks.

[1]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[2]  Tzong-Jer Chen,et al.  Fuzzy c-means clustering with spatial information for image segmentation , 2006, Comput. Medical Imaging Graph..

[3]  D. Louis Collins,et al.  Twenty New Digital Brain Phantoms for Creation of Validation Image Data Bases , 2006, IEEE Transactions on Medical Imaging.

[4]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[6]  Daoqiang Zhang,et al.  Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation , 2007, Pattern Recognit..

[7]  D. Louis Collins,et al.  A new improved version of the realistic digital brain phantom , 2006, NeuroImage.

[8]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  S.M. Szilagyi,et al.  MR brain image segmentation using an enhanced fuzzy C-means algorithm , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[10]  Jae S. Lim,et al.  Two-Dimensional Signal and Image Processing , 1989 .

[11]  James C. Bezdek,et al.  Partially supervised clustering for image segmentation , 1996, Pattern Recognit..

[12]  Sherin M. Youssef,et al.  Dynamically Adaptive Data Clustering Using Intelligent Swarm-like Agents , 2007 .

[13]  Miin-Shen Yang,et al.  A similarity-based robust clustering method , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  K. Laws.  Textured Image Segmentation , 1980 .

[15]  Nikos A. Vlassis,et al.  The global k-means clustering algorithm , 2003, Pattern Recognit..