Feature Extraction of MRI Brain Images for the Early Detection of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a common form of senile dementia. Although the understanding of key steps underlying neurodegeneration in Alzheimer’s disease (AD) is incomplete, it is clear that it begins long before symptoms are noticed by patient. Conventional clinical decision making systems are more manual in nature and ultimate conclusion in terms of exact diagnosis is remote. In this case, the employment of advanced Biomedical Engineering Technology will definitely helpful for making diagnosis. Any disease modifying treatments which are developed are most possibly to be achieving success if initiated early in the process, and this needs that we tend to develop reliable, validated and economical ways to diagnose Alzheimer’s kind pathology. However, despite comprehensive searches, no single test has shown adequate sensitivity and specificity, and it is likely that a combination will be needed. Profiling of human body parameter using computers can be utilised for the early diagnosis of Alzheimer’s disease. There are several imaging techniques used in clinical practice for the diagnosis of Alzheimer’s type pathology. There are lot of tests and neuroimaging modalities to be performed for an effective diagnosis of the disease. Prominent of them are Magnetic Resonance Imaging Scan (MRI), Positron Emission Tomography (PET), Single Photon Emission CT Scanning (SPECT), MRI Imaging and Optical Coherence Tomography (OCT). In this research we have proposed a new scheme based on Wavelet Networks (WN) for the feature extraction of MRI brain images for the early diagnosis of AD. The database of MRI images were obtained from Sree Gokulam Medical College and Research Foundation (SGMC&RF), Trivandrum, India.