Brain tumor detection and classification is the most difficult and tedious task in the area of medicinal image preparing. MRI (Magnetic Resonance Imaging) is a medicinal procedure, generally adopted by the radiologist for representation of inner structure of the human body with no surgery. MRI gives abundant data about the human delicate tissue, which helps in the conclusion of brain tumor. MRI is used for detection because of its superior image resolution, speed of acquisition, and high safety profile for patients [19]. Precise segmentation of MRI image is basic for the conclusion of brain tumor by computer supported clinical device. This paper is focused towards the design of an optimal and more accurate way for the detection of tumor from brain MRI scans and if it confirms the presence of tumor then it is focused on evaluating its stage i.e. benign or malignant using classification via linear classifier SVM. The method which we proposed consists of pre-processing using the histogram and morphological operation then will perform segmentation using Particle Swarm Optimization (PSO), feature extraction using GLCM, reduction of the features using PCA, to reduce the feature set still more we are using ICA (Independent component Analysis) as it extracts independent components for extracted GLCM and for the classification the SVM classification. The results are simulated in the MATLAB2015.
Volume 11 | 08-Special Issue
Pages: 1310-1320