A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification

Abstract

In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

“…MRI Segmentation with Rough-Fuzzy C-Means Segmentation is a crucial step in the analysis of brain tumor MRI scans, aiding in the identification and isolation of tumor cells [2]. Bal and colleagues introduced a segmentation method utilizing rough-fuzzy Cmeans and shape-based properties [3].…”

“…In general, this study issue intends to contribute to the development of advanced tools and methodologies for the interpretation of brain tumor MRI scans, which may ultimately result in improved patient outcomes. (see, for example, [1][2][3]).…”