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

TLDR

MRI intensity inhomogeneities arise from coil imperfections or acquisition issues, producing a slowly varying shading artifact that can mislead conventional intensity‑based classification. The study proposes a novel fuzzy segmentation algorithm for MRI that estimates intensity inhomogeneities using fuzzy logic. The algorithm modifies the FCM objective function to incorporate a neighborhood regularizer that biases voxel labeling toward piecewise‑homogeneous assignments, thereby compensating for intensity inhomogeneities. The regularization improves segmentation of salt‑and‑pepper corrupted scans, and experiments on synthetic and real MR images demonstrate the algorithm’s effectiveness and efficiency.

Abstract

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.