A modified fuzzy C-means algorithm for MR brain image segmentation

Authors:
László Szilágyi;Sándor M. Szilágyi;Zoltán Benyó
Affiliations:
Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Budapest, Hungary and Sapientia - Hungarian Science University of Transylvania, Facul ...;Sapientia - Hungarian Science University of Transylvania, Faculty of Technical and Human Science, Romania;Budapest University of Technology and Economics, Department of Control Engineering and Information Technology, Budapest, Hungary
Venue:
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
Year:
2007

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Abstract

Automated brain MR image segmentation is a challenging pattern recognition problem that received significant attention lately. The most popular solutions involve fuzzy c-means (FCM) or similar clustering mechanisms. Several improvements have been made to the standard FCM algorithm, in order to reduce its sensitivity to Gaussian, impulse, and intensity non-uniformity noises. This paper presents a modified FCM-based method that targets accurate and fast segmentation in case of mixed noises. The proposed method extracts a scalar feature value from the neighborhood of each pixel, using a context dependent filtering technique that deals with both spatial and gray level distances. These features are clustered afterwards by the histogram-based approach of the enhanced FCM algorithm. Results were evaluated based on synthetic phantoms and real MR images. Test experiments revealed that the proposed method provides better results compared to other reported FCMbased techniques. The achieved segmentation and the obtained fuzzy membership values represent excellent support for deformable contour model based cortical surface reconstruction methods.