Bias-Corrected Intuitionistic Fuzzy C-Means With Spatial Neighborhood Information Approach for Human Brain MRI Image Segmentation

Segmentationof MRI imagesin the presence of artifacts such as noise and bias field effect (BFE) is challenging. In this article, to handle both artifacts, we have formulated an intuitionistic fuzzy set (IFS) theory based bias-corrected intuitionistic fuzzy c-means with spatial neighborhood information (SNI) method for magnetic resonance imaging (MRI) image segmentation. We represent the image in terms of IFSs using two well-known intuitionistic fuzzy generation functions, namely Sugeno's negation function (SNF) and Yager's negation function (YNF). In this way, the proposed approach takes advantage of the IFS theory to address the uncertainty and vagueness present in data. The SNI term helps to retain the fine image details in the segmentation process, which is usually lost when smoothing is used. The BFE is modeled as a slow varying intensity field additive in nature with mean zero in IFS framework. The proposed approach estimates the bias field and produces a segmented image simultaneously. The segmentation performance of the proposed method is evaluated in terms of the Dice score, average segmentation accuracy (ASA), and index of variation. Further, the comparison of the proposed method with other similar state-of-the-art methods on two well-known publicly available Brain MRI datasets shows the significant improvements in segmentation performance in terms of ASA, Dice score, and index of variation. The proposed approach achieves 93% ASA in the presence of severe bias field and noise artifacts on BrainWeb simulated dataset, which outperformed the state-of-the-art methods.