Abstract
There is a great benefit of Alzheimer disease (AD) classification for health care application. AD is the most common form of dementia. This paper presents a new methodology of invariant interest point descriptor for Alzheimer disease classification. The descriptor depends on the normalized Hu Moment Invariants (NHMI). The proposed approach deals with raw Magnetic Resonance Imaging (MRI) of Alzheimer disease. Seven Hu moments are computed for extracting images’ features. These moments are then normalized giving new more powerful features that highly improve the classification system performance. The moments are invariant which is the robustness point of Hu moments algorithm to extract features. The classification process is implemented using two different classifiers, K-Nearest Neighbors algorithm (KNN) and Linear Support Vector Machines (SVM). A comparison among their performances is investigated. The results are evaluated on Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The best classification accuracy is 91.4% for KNN classifier and 100% for SVM classifier.
Highlights
Alzheimer's disease (AD) is a permanent, progressive neurological brain disorder and complex disease which gradually destroys brain cells, reducing memory and thinking ability causing the dead, and eventually loss of the capability to perform even the simplest tasks
Alzheimer database images in this work have been classified into two classes: normal cognitive (MCI) and brain suffered from Alzheimer disease (AD)
Support Vector Machines (SVM) classifier has reached its best performance once we normalize the Hu moments no matter how much we increase the training datasets
Summary
Alzheimer's disease (AD) is a permanent, progressive neurological brain disorder and complex disease which gradually destroys brain cells, reducing memory and thinking ability causing the dead, and eventually loss of the capability to perform even the simplest tasks. AD was named after the German psychoanalyst and pathologist Alois Alzheimer when he tested a female patient (post mortem) in 1906 [2]. Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to examine the body‟s anatomy and physiology in both healthy and diseased patients [4]. MRI scans can offer a utilitarian tool for estimating the properties of anti-dementia drugs in clinical tests which can highly serve the researchers in these fields. Scans can provide information about the levels and location of cell damage over time, and that would help to get valuable information about the optimistic effects of potential treatments
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