Analysis on Various Feature Extraction Methods for Medical Image Classification

Abstract

Soft Computing is an emerging technique of machine intelligence that includes methods like Neural Networks, Fuzzy Logic, Support Vector Machines and Genetic Algorithms. The aim of soft computing is to mimic the reasoning and decision making of human. Neural networks are prominently being used in applications that involve processing of voluminous data and hence it can be highly applicable to the areas like image processing, stock market prediction and weather forecasting. Medical image processing involves four phases like Preprocessing, Segmentation, Feature Extraction and Classification. The aim of this work is to find the feature extraction method that is best for classifying the medical images. Local Binary Patterns (LBP), Gray-Level-Run-Length-Matrix (GLRM), Completed Local Binary Patterns (CLBP), Gray-Level Co-occurrence Matrix (GLCM) and Local Tetra Patterns (LTrP) are the most prominent feature extraction methods for medical images and are considered in this study. Two well-known classifiers Multi-Layer Perceptron using Backpropagation Network (MLPBPN) and Support Vector Machine (SVM) are used to analyse the efficiency of above specified five feature extraction techniques. Five different medical image Datasets are considered for experimentation. The experimental results illustrate that GLCM method is the best method compared with the other four feature extraction methods for medical image classification.