Alzheimer's Disease Segmentation and Classification on MRI Brain Images Using Enhanced Expectation Maximization Adaptive Histogram (EEM-AH) and Machine Learning

Alzheimer’s disease (AD) is an irreversible ailment. This ailment causes rapid loss of memory and behavioral changes. Recently, this disorder is very common among the elderly. Although there is no specific treatment for this disorder, its diagnosis aids in delaying the spread of the disease. Therefore, in the past few years, automatic recognition of AD using image processing techniques has achieved much attraction. In this research, we propose a novel framework for the classification of AD using magnetic resonance imaging (MRI) data. Initially, the image is filtered using 2D Adaptive Bilateral Filter (2D-ABF). The denoised image is then enhanced using Entropy-based Contrast Limited Adaptive Histogram Equalization (ECLAHE) algorithm. From enhanced data, the region of interest (ROI) is segmented using clustering and thresholding techniques. Clustering is performed using Enhanced Expectation Maximization (EEM) and thresholding is performed using Adaptive Histogram (AH) thresholding algorithm. From the ROI, Gray Level Co-Occurrence Matrix (GLCM) features are generated. GLCM is a feature that computes the occurrence of pixel pairs in specific spatial coordinates of an image.  The dimension of these features is reduced using Principle Component Analysis (PCA). Finally, the obtained features are classified using classifiers. In this work, we have employed Logistic Regression (LR) for classification. The classification results were achieved with the accuracy of 96.92% from the confusion matrix to identify the Alzheimer’s Disease. The proposed framework was then evaluated using performance evaluation metrics like accuracy, sensitivity, F-score, precision and specificity that were arrived from the confusion matrix. Our study demonstrates that the proposed Alzheimer’s disease detection model outperforms other models proposed in the literature.