A novel fuzzy energy based level set method for medical image segmentation

Abstract

Segmentation is a very important step in the field of image processing. Noise and intensity inhomogeneity make challenging the segmentation of images, especially for medical images. Fuzzy C-means (FCM) clustering is one of the most widely used methods in medical image segmentation, but it can not deal effectively with noise and intensity inhomogeneity. Accurate segmentation capability of level set-based active contour models make them attractive in medical image analysis but they also fail to perform better when medical images are corrupted by noise. To deal with Gaussian noise and intensity inhomogeneity, a new region-based level set model is proposed by integrating active contour and FCM clustering. In this method, FCM-based energy function is used with level set method to overcome local minimum problem of active contour modal. Distance Regularized Level Set Evolution (DRLSE) is used in proposed method to deal with re-initialization problem of traditional level set method. These two modifications in level set modal effectively deal with intensity inhomogeneity of medical image. A mean filter-like spatial term is also utilized with the proposed energy function, which makes this method advantageous for segmenting noisy images. The planned scheme is verified on diverse real medical images and synthetic images, which contain noise as well as intensity inhomogeneity. The proposed method is compared with other state-of-the-art methods in terms of Segmentation Accuracy, Precision, and Recall. Results show that the proposed method offers better performance compared to other latest methods for segmentation of noisy images.