Abstract
Abstract Computer-assisted tools can aid in the detection of Alzheimer disease (AD) which is a progressive neurodegenerative disorder that can lead to cognitive impairments and eventually death. The accumulated effects due to AD can cause changes in the appearance of grey matter, white matter and cerebrospinal fluid in brain Magnetic Resonance (MR) images. This study aims to use Kernel Density Estimation (KDE) technique to analyse the textural changes from single slice brain MR images for the detection of AD. The preprocessed, skull stripped T1-weighted MR brain images are obtained from the publicly available OASIS database. A single axial slice per subject is chosen from a volumetric image for further processing to reduce the computational load. Multivariate KDE technique is applied to each pixel, by considering the changes in the neighbourhood based on selected bandwidth to obtain corresponding density estimates. Statistical features quantifying the distribution of density estimates are extracted to characterise textural variations in images. Linear discriminant analysis (LDA) classifier is implemented with ten-fold cross-validation for detecting AD. An optimum bandwidth of 18 for the KDE technique is selected based on the classification performance. Out of seven extracted texture features, three are found to be statistically significant in distinguishing AD. The classification with LDA yields an accuracy of 72.3% with a sensitivity of 80.6% for identifying AD from healthy subjects. The proposed method is efficient in detecting AD by revealing the textural changes within the brain slice without the involvement of any segmentation technique. Thus, the novel KDE-based texture analysis proves to be an effective tool for the automated diagnosis of AD from single slice brain MR images.
Highlights
Alzheimer’s disease is the most common form of dementia, accounting for nearly 70% of all cases
Images used in this study consists of T1-weighted Magnetic Resonance (MR) transaxial brain images of 92 normal and 92 Alzheimer disease (AD) subjects obtained from publicly available Open Access Series of Imaging Studies (OASIS) cross-sectional database [13]
The representative brain MR images of normal and AD subjects are shown in Figure.2 (a) and (b), respectively
Summary
Alzheimer’s disease is the most common form of dementia, accounting for nearly 70% of all cases. It is a progressive neurodegenerative disorder that is increasingly becoming a lethal and burdening disease in the ageing world population [1]. It is characterised by symptoms such as deterioration in memory and cognitive skills, behavioural deficits, and inability to perform daily activities as the severity increases. In recent decades neuroimaging techniques are becoming a popular non-invasive way of detecting AD. The most commonly used techniques are - computed tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) imaging modalities. MRI is most prevalent among these techniques due to its high tissue contrast, spatial resolution, low cost, and widespread availability [3]
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