Intensity inhomogeneity compensation and segmentation of MR brain images using hybrid c-means clustering models
Medical image segmentation and registration problems based on magnetic resonance imaging are frequently disturbed by the intensity inhomogeneity or intensity non-uniformity (INU) of the observed images. Most compensation techniques have serious difficulties at high amplitudes of INU. This study proposes a multiple stage hybrid c-means clustering approach to the estimation and compensation of INU, by modeling it as a slowly varying additive or multiplicative noise. The slowly varying behavior of the estimated inhomogeneity field is assured by a context sensitive smoothing filter based on a morphological criterion. The qualitative and quantitative evaluation using 2-D synthetic phantoms and real T1-weighted MR images place the proposed methodology among the most accurate segmentation techniques in the presence of high-magnitude inhomogeneity.
intensity inhomogeneity hybrid c-means clustering image segmentation magnetic resonance imaging fuzzy c-means clustering context dependent filter morphological operations