A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data

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...

• Type: Journal
• Year: 2002
• Where: TMI
• Authors: Mohamed N. Ahmed, Sameh M. Yamany, Nevin Mohamed, Aly A. Farag, Thomas Moriarty
• Keywords: Algorithm, Intensity Inhomogeneities, MRI Intensity Inhomogeneities, TMI 2002