Improved Rough-fuzzy C-means Clustering and Optimum Fuzzy Interference System for MRI Brain Image Segmentation

Abstract

The categorization of brain tissues plays a vital role in various neuro-anatomical identification and implementations. In manual detection, misidentification of location and sound of unwanted tissues may occur due to visual fatigue by humans. Also, it consumes more time and may exhibit enormous partially inner or outer the manipulator. At present, automatic identification of brain tissues in MRI is vital for investigation and healing applications. This work proposed MRI image tissue segmentation using Improved Rough Fuzzy C Means (IRFCM) algorithm and classification using multiple fuzzy systems. Proposed research work comprises four modules: pre-processing, segmentation, categorization, and extracting features. Initially, the elimination of boisterous occur in the given image is done through pre-processing. After the pre-processing, segmentation is carried out for the pre-processed brain image to segment the tissue based on clustering concept using Improved Rough Fuzzy C Means algorithm. Later, the features of Gray-Level Co-Occurrence Matrix (GLCM) are extracted from segmentation, and the features extracted from segmented images are applied to Optimum Fuzzy Interference System (OFIS). Then the entire system parameters are optimized using Enhanced Grasshopper Optimization Algorithm (EGOA). Finally, the novel OFIS classifier helps to classify the brain-based tissue images as Gray Matter (GM), White Matter (WM), Cerebrospinal Fluid (CSF), and Tumor Tissues (TT). The results using MRI data sets are analyzed and compared with other existing techniques through performance metrics to show the superiority of the proposed methodology.