Detection of Brain Tumour in MRI Scan Images using Tetrolet Transform and SVM Classifier

Abstract

Objectives: To design a system with high accuracy, sensitivity and specificity to detect brain tumour. Methods/ Statistical Analysis: Over the years, extensive research works have been done for brain tumour diagnosis using various imaging modalities. Magnetic Resonance Imaging (MRI) method has been proven to be effective in imaging the human brain for detection and classification of the tumours for clinical diagnosis. In this paper, Tetrolet Transform (TT) and Support Vector Machine (SVM) Classifier have been utilized for recognition of tumour from brain MRI images. Findings: The brain MRI image is transformed into a set of features that best describes the information content of the image. So as to use the Tetrolet transform for recognition of tumour, MRI brain tumour image is decomposed by utilizing the Tetrolet transform at a predefined level. Statistical features such as mean, standard deviation, and variance of each tetromino sub bands are computed and extracted. The t-test class separability model is applied to the calculated features keeping in mind the objective to choose the best features from the database images. The receiver operating characteristics (ROC) curves and confusion matrix are make use of, to decide the accuracy level of the proposed method. The experimental results show an accuracy level of 98.8% with fifth level of decomposition and SVM based classification while testing with images from Repository of Molecular Brain Neoplasia Data (REMBRANDT) database. The performance of the proposed system have been evaluated by using the parameters namely- accuracy, sensitivity and specificity. Application/Improvements: This technique is applied to the training images and the selected features are put away in a feature vector. 10-fold cross validation method is utilized to test viability of the proposed technique.