Hybrid Feature Extraction Technique on Brain MRI Images for Content-Based Image Retrieval of Alzheimer’s Disease

Abstract

Alzheimer is a prominent cause of death, ranked sixth in the list. Timely diagnosis of such abnormalities may help to temporarily minimize its worsening. Computer-aided techniques are adapted to the brain MR images for diagnosing and retrieval of Alzheimer disease images. An immense amount of work has been carried on the generic image retrieval systems using content-based information. These image retrieval schemes have their own merits and demerits in their retrieval performance. So, it is required to develop an efficient content-based image retrieval (CBIR) system in the medical field which is still a challenging task. Therefore, a hybrid features extraction technique has been proposed for CBIR wherein contrast feature, texture-based features and morphological operated features of brain MRI images are extracted and these features are hybridized by applying fusion technique for better Alzheimer disease detection. The proposed approach of feature extraction techniques is evaluated using support vector machine (SVM) and decision tree (DT) classification scheme for pattern learning and classification. Based on the results of feature extraction techniques, SVM and DT achieve an overall accuracy of 91.25 and 86.66% with better precision, recall, sensitivity and specificity.