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MRI Brain Cancer Diagnosis Approach Using Gabor Filter and Support Vector Machine

Feature extraction is a very important and crucial stage in recognition system. It has been widely used in object recognition, image content analysis and many other applications. Feature extraction is the best way/method to recognize images in the field of medical images. However, the selection of proper feature extraction method is equally important because the classifier output depends on the input features. This study proposes an image classification methodology that automatically classifies human brain magnetic resonance (MR) images. The proposed method consists of four main stages: preprocessing, feature extraction, feature reduction and classification, followed by evaluation. The first stage starts with noise reduction in MR images. In the second stage, the features related to MR images are obtained using Gabor filter. In the third stage, the features of MR images are reduced to the more essential features using kernel linear discriminator analysis (KLDA). In the last stage, the classification stage, two classifiers have been developed to classify subjects as normal or abnormal MRI human images. Whereas the first classifier is based on Support Vector Machine (SVM), the second classifier is based on K-Nearest Neighbor (KNN) on Euclidean distance. Classification accuracies are 100% and 96.3% for SVM and KNN classifiers respectively. The result shows that the proposed methodologies are robust and effective compared with other existing technologies in classi ﬁcation of MRI tumor brain