An Efficient Classification of MRI Brain Images

Abstract

The unprecedented improvements in computing capabilities and the introduction of advanced techniques for the analysis, interpretation, processing, and visualization of images have greatly diversified the domain of medical sciences and resulted in the field of medical imaging. The Magnetic Resonance Imaging (MRI), an advanced imaging technique, is capable of producing high quality images of the human body including the brain for diagnosis purposes. This paper proposes a simple but efficient solution for the classification of MRI brain images into normal, and abnormal images containing disorders and injuries. It uses images with brain tumor, acute stroke and alzheimer, besides normal images, from the public dataset developed by harvard medical school, for evaluation purposes. The proposed model is a four step process, in which the steps are named: 1). Pre-processing, 2). Features Extraction, 3). Features Reduction, and 4). Classification. Median filter, being one of the best algorithms, is used for the removal of noise such as salt and pepper, and unwanted components such as scalp and skull, in the pre-processing step. During this stage, the images are converted from gray scale to colored images for further processing. In second step, it uses Discrete Wavelet Transform (DWT) technique to extract different features from the images. In third stage, Color Moments (CMs) are used to reduce the number of features and get an optimal set of characteristics. Images with the optimal set of features are passed to different classifiers for the classification of images. The Feed Forward - ANN (FF-ANN), an individual classifier, which was given a 65% to 35% split ratio for training and testing, and hybrid classifiers called: Random Subspace with Random Forest (RSwithRF) and Random Subspace with Bayesian Network (RSwithBN), which used 10-Fold cross validation technique, resulted in 95.83%, 97.14% and 95.71% accurate classification, in corresponding order. These promising results show that the proposed method is robust and efficient, in comparison with, existing classification methods in terms of accuracy with smaller number of optimal features.

Keywords

• Color moments (CMs)
• feed forward artificial neural network (FF-ANN)
• random subspace
• random forest
• baysnet
• principle component analysis (PCA)