Inhomogeneity correction for magnetic resonance images with fuzzy C-mean algorithm

Segmentation of magnetic resonance (MR) images plays an important role in quantitative analysis of brain tissue morphology and pathology. However, the inherent effect of image-intensity inhomogeneity renders a challenging problem and must be considered in any segmentation method. For example, the adaptive fuzzy c-mean (AFCM) image segmentation algorithm proposed by Pham and Prince can provide very good results in the presence of the inhomogeneity effect under the condition of low noise levels. Their results deteriorate quickly as the noise level goes up. In this paper, we present a new fuzzy segmentation algorithm to improve the noise performance of the AFCM algorithm. It achieves accurate segmentation in the presence of inhomogeneity effect and high noise levels by incorporating the spatial neighborhood information into the objective function. This new algorithm was tested by both simulated experimental and real clinical MR images. The results demonstrated the improved performance of this new algorithm over the AFCM in the clinical environment where the inhomogeneity and noise levels are commonly encountered.