Alzheimer’s disease is the most common form of dementia occurring in the elderly persons. Its early diagnosis may help in providing proper treatment. To date, there is no appropriate technique available to automatically classify it using MR brain images. In this work, first-and-second-order-statistics (FSOS) was employed for classification of Alzheimer’s from T2 trans-axial brain MR images. Although FSOS is a simple and well known feature extraction technique, it is not yet explored for Alzheimer’s classification. Performance of FSOS was compared with the state-of-the-art feature extraction techniques. Five commonly used classifiers were employed to build decision models. The performance of the models was evaluated in terms of sensitivity, specificity, accuracy, F-measure, training, and testing time. These models were built with varying number of training samples. Results showed that FSOS outperforms all the other existing feature extraction techniques in terms of all the considered performance measures. This was also validated by a statistical test. Interestingly, it was found that FSOS gives high performance irrespective of the choice of classifier and it works well even on small available number of samples, which is usually desired for all real time problems.Keyword: Discrete Wavelet Transform, Feature Extraction, First and Second Order Statistics, Gabor Transform, Magnetic Resonance Imaging, Slantlet Transform
Segmentation of brain MR images using fuzzy sets and modified co-occurrence matrix
