A Fuzzy-Entropy and Image Fusion Based Multiple Thresholding Method for the Brain Tumor Segmentation

This research presented a new segmentation method based on fuzzy set, entropy and image fusion to analyze brain tumors from magnetic resonance imaging (MRI). Using fuzzy set, one can tackle the problem of uncertainty representation in gray levels of MRIs during the segmentation process. This uncertainty in their gray levels occurred due to poor illumination of images. To resolve this issue, this study focused on fuzzification of gray levels and assignment of membership degrees based on membership functions. Each fuzzified gray level value was quantified using entropy. The proposed method generated multiple thresholds based on maximum entropy values of gray levels. These thresholds generated multiple segmented images with different features. Finally, image fusion operation was performed on multiple segmented images to highlight all the critical features of brain tumors. Fusion images were compared with the segmented images obtained from four additional methods, the multilevel threshold method, adaptive threshold method, K-means clustering algorithm and fuzzy c-means algorithm. The performance evaluation metrics indicated the effectiveness of the proposed method over these existing methods.