Generalized rough fuzzy c-means algorithm for brain MR image segmentation

Authors:
Zexuan Ji;Quansen Sun;Yong Xia;Qiang Chen;Deshen Xia;Dagan Feng
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China and Biomedical and Multimedia Information Technology (BMIT) Research Group, School o ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, NSW 2006, Australia and School of Computer Science, Northweste ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China;Biomedical and Multimedia Information Technology (BMIT) Research Group, School of Information Technologies, The University of Sydney, NSW 2006, Australia and Center for Multimedia Signal Processin ...
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

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Abstract

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less accurate results. In this paper, a novel hybrid clustering approach, namely the generalized rough fuzzy c-means (GRFCM) algorithm is proposed for brain MR image segmentation. In this algorithm, each cluster is characterized by three automatically determined rough-fuzzy regions, and accordingly the membership of each pixel is estimated with respect to the region it locates. The importance of each region is balanced by a weighting parameter, and the bias field in MR images is modeled by a linear combination of orthogonal polynomials. The weighting parameter estimation and bias field correction have been incorporated into the iterative clustering process. Our algorithm has been compared to the existing rough c-means and hybrid clustering algorithms in both synthetic and clinical brain MR images. Experimental results demonstrate that the proposed algorithm is more robust to the initialization, noise, and bias field, and can produce more accurate and reliable segmentations.