A Modified Method for Brain MRI Segmentation Using Dempster-Shafer Theory

In any kind of medical imaging, segmentation plays a crucial part. To detect brain tissues or tumors, brain MRI segmentation is the safest way. But brain MRI segmentation is not an easy task because of various kinds of artifacts such as intensity non-uniformity, partial volume effects, and noise. Brain MRI artifacts lead to uncertainty in pixel values of the different parts of MRI modalities, which include T1- and T2-weighted, Proton density (PD) and Flair images. In this paper, the proposed method has followed two main steps. Firstly, Brain MRI is divided into some clusters using Fuzzy c-mean (FCM) then Fuzzy clustering and specific mapping were used to form the Dempster-Shafer belief structure. The purpose of this research is to combine Dempster- Shafer theory and fuzzy clustering which gives a technique based on data fusion of different modalities by considering spatial neighborhood information. The results of the proposed method have been evaluated by using Dice and Jaccard coefficients which prove its superiority over other benchmark methods.