Abstract
An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.
Highlights
Support Vector Machine (SVM) is supervised classification technique, which is based on theory of statistical learning
The margins must be set Various techniques for extracting features from MRI brain images have been reported in the literature, the most common are: Discrete Wavelet Transform (DWT) [9], Gabor filters [10] and Gray Level Co-occurrence Matrix (GLCM) [11]
The test set for this evaluation experiment were run on MR Images procured from hospital
Summary
Support Vector Machine (SVM) is supervised classification technique, which is based on theory of statistical learning. The basic concept of SVM is based on binary classification as it separates data points by a straight line to classify the class label. SVM is one of the few machine learning algorithms to address the generalization problem (i.e., how well a derived model will perform on unseen data). According to Novikoff’s theorem, minimizing the generalization error is equivalent to maximizing the separating margin in support vector classification (SVC)
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