An Efficient Threshold And Region-Based Approach: Brain MRI Segmentation Using Fuzzy C-Means Adaptive Convolutional Long/Short-Term Memory

Abstract

All body organs, especially the most important ones, are controlled by the brain, as well as any dysfunction of these cells would immediately jeopardize the life of a person by causing other organs to collapse. The brain is regarded as the most important organ in a person's body as a result. A tumor is a disorder of the brain's cells that manifests as inflamed brain tissue. The likelihood of quickly curing sickness will increase with the early identification of such tumorous cells. The detection of brain tumors is now done using magnetic resonance imaging (MRI). Several approaches have been created for MRI segmentation and tumor detection in the newly emerging study subject of image processing and segmentation of MRI images. An efficient threshold and region-based segmentation technique termed fuzzy c-means adaptive convolutional long/short-term memory (FCM-CLSTM) is proposed in this study, along with various morphological operations. To improve the MRI image quality, the following methods are first applied: normalized median filter (NMF), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE). The pixels are then divided into distinct classes using our suggested FCM-CLSTM approach's threshold and region-based segmentation, classification, and morphological operators, which are then used to find the tumor portion of the image with the highest intensity. The analysis is done on accuracy (88.24%), precision (87.66%), recall (87.66%), F1-score (87.66%), RMSE (0.3430), AMBE (0.0) and MSE (0.1176) with the existing methodologies. The findings of the proposed method achieved significant scores compared to the existing method in brain MRI segmentation.