Discrimination between alzheimer´s disease and control group in MR-images based on texture analysis using artificial neural network

In this study, we have proposed a novel method investigates MR-Images for normal and abnormal brains which effected by Alzheimer\´s Disease (AD) to extract 336 number of different features based on texture analysis. Before applying this algorithm, we have to use a registration method because of variety in size of normal and abnormal images. Consequently, the output of Texture Analysis System (TAS) is a vector containing 336 elements that are features extracted from texture. This vector is considered as the input of the Artificial Neural Network (ANN) which is feed-forward one. The features extracted from the Gray-level Co-occurrence Matrix (GLCM) have been interpreted and compared with normal brains to make the final decision. Decision making is the role of feed-forward ANN. Before using ANN, we have applied Principle Component Analysis (PCA) to eliminate any redundancies in features i.e. input vector. The results show a powerful diagnosis of AD with 95 percent proper response. Dataset includes 75 MR-images: 50 images show normal brains and rest of them is affected by AD. We used 60 percent of every group for training and 40 percent were considered as a "test data".