Brain tumor segmentation using cellular automata-based fuzzy c-means

Abstract

This paper presents a novel brain tumor segmentation method. It is a hybrid of fuzzy c-means clustering algorithm (FCM) and cellular automata model (CA) through the features obtained from gray level co-occurrence matrix (GLCM). This aims to improve the seed growing problem using similarity function generally found in traditional segmentation algorithms. The drawback of traditional similarity function being defined as a distance of pairwise pixels faces the problem of robustness when growing pixels are moving from the seeds. To cope with this problem, fuzzy membership functions obtained by FCM is applied. For performance evaluation, BraTS2013 dataset is empirically experimented throughout in comparisons with the promising compared methods using dice similarity metrics. In this regard, the proposed method shows the outstanding results superior to the compared methods on average.