A fuzzy c-means (FCM) based algorithm for intensity inhomogeneity correction and segmentation of MR images

Magnetic resonance images are often corrupted by intensity inhomogeneity, which manifests itself as slow intensity variations of the same tissue over the image domain. Such shading artifacts must be corrected before doing computerized analysis such as intensity-based segmentation and quantitative analysis. In this paper, we present a fuzzy c-means (FCM) based algorithm that simultaneously estimates the shading effect while segmenting the image. A multiplier field term that models the intensity variation is incorporated into the FCM objective function which is minimized iteratively. In each iteration, the bias field is estimated based on the current tissue class centroids and the membership values of the voxels and then smoothed by an iterative low-pass filter. The efficacy of the algorithm is demonstrated on clinical breast MR images.