A Multilevel De-Noising Approach for Precision Edge-Based Fragmentation in MRI Brain Tumor Segmentation

Abstract

Brain tumors, the second leading cause of mortality as identified by numerous health agencies, constitute a significant health challenge. Given the integral role of the brain in governing essential functionalities, such as memory, vision, learning, and problem-solving, early detection of malignant brain tumors is critical for effective medical intervention. Magnetic Resonance Imaging (MRI), demonstrating superior precision and reliability over Computed Tomography (CT), is a preferred modality for brain cancer identification. This study introduces an innovative approach to brain tumor detection and segmentation, utilizing fragmentation. Fragmentation, a promising method for brain cancer analysis, involves the differentiation of cancerous tissue from other brain components, such as fatty tissue, edema, normal brain matter, and cerebrospinal fluid. The critical role of denoising in this process, which entails iterative thresholding until the tumor is appropriately segregated, is examined. Further, the use of image classification for distinguishing abnormal regions indicative of a brain tumor in MRI scans is outlined. The proposed Multilevel De-noising model with Precision Edge-based Fragmentation for Tumor Size Diagnosis (MD-PES-TSD) is presented as a comprehensive framework for the detection and segmentation of MRI images. The MD-PES-TSD model is designed to effectively reduce image noise, identify structural brain edges, differentiate between abnormal and normal brain regions, and ultimately determine the size of the tumor. An evaluation of MRI data scans segregated into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) regions, following the implementation of early-stage denoising in the pre-processing phase and feature extraction through segmentation, is conducted. The MD-PES-TSD model is shown to outperform existing models in comparative analysis, signifying its potential as an effective solution for brain tumor detection and segmentation.