title: A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data.
abstract: In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data and estimation of intensity inhomogeneities using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the radio-frequency coils or to problems associated with the acquisition sequences. The result is a slowly varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution toward piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. Experimental results on both synthetic images and MR data are given to demonstrate the effectiveness and efficiency of the proposed algorithm.
authors: Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt.
authors: Mohamed N
authors: MN
authors: Ahmed
authors: Sameh M
authors: SM
authors: Yamany
authors: Nevin
authors: N
authors: Mohamed
authors: Aly A
authors: AA
authors: Farag
authors: Thomas
authors: T
authors: Moriarty
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D001932:Brain Neoplasms / Q000175:diagnosis; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D013269:Stochastic Processes