Robust level set image segmentation algorithm using local correntropy-based fuzzy c-means clustering with spatial constraints

Accurate image segmentation is a challenge task in image analysis and understanding, while fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for this challenge. However, FCM_S has high computational complexity and still lacks enough robustness to noise and outliers, which will limit its usefulness. To overcome these difficulties, a local correntropy-based fuzzy c-means clustering algorithm with spatial constraints (LCFCM_S) and its simplified model (LCFCM_S1) are proposed in this paper. By utilizing the correntropy criterion, the clustering algorithm can efficiently emphasize the weights of the samples that are close to their corresponding cluster centers. Then, the proposed clustering algorithms are incorporated into a variational level set formulation with a level set regularization term. Finally, the iteratively re-weighted algorithm is adopted to solve the LCFCM_S and LCFCM_S1 based level set method. Experimental results on synthetic and real images show the superiority of our methods in terms of accuracy and robustness for segmenting images with intensity inhomogeneity and noise, when compared with several state-of-the-art approaches.