Fuzzy local intensity clustering (FLIC) model for automatic medical image segmentation

Abstract

Intensity inhomogeneity is one of the main challenges in automatic medical image segmentation. In this paper, fuzzy local intensity clustering (FLIC), which is based on the combination of level set algorithm and fuzzy clustering, is proposed to mitigate the effect of intensity variation and noise contamination. For the FLIC method, the segmentation and bias modification are carried out in a fully automatic and simultaneous manner through the local clustering of intensity and selection of the initial contour by the fuzzy method. Besides, the local entropy is integrated into the FLIC function to improve the contour evolution. Experimental results on inhomogeneous medical images indicate the superiority of the FLIC model over the other state-of-the-art segmentation methods in terms of accuracy, robustness, and computational time.