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Alzheimer disease (AD) is known as the most common form of dementia, which imposes a considerable burden on society. In this paper, we focus on the automated diagnosis of Alzheimer disease. Based on the researches on neuropathology, we adopt the thickness of cortex regions from the magnetic resonance imaging (MRI) to characterize the pathology of AD. 3D reconstruction technique is utilized to extract...
In multi-atlas based segmentation propagation, segmentations from multiple atlases are propagated to the target image and combined to produce the segmentation result. Local weighted voting (LWV) method is a classifier fusion method which combines the propagated atlases weighted by local image similarity. We demonstrate that the segmentation accuracy using LWV improves as the number of atlases increases...
Whole brain extraction is an important pre-processing step in neuro-image analysis. We compared the accuracy of four automated brain extraction methods: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), Hybrid Watershed Algorithm (HWA) and a Multi-Atlas Propagation and Segmentation (MAPS) technique we have previously developed for hippocampal segmentation. The four methods were applied to...
As medical imaging datasets continue to grow, interest in effective ways to analyze the statistical properties and data variability within those datasets has surged. Accurate analysis of the morphological statistical properties of a group of images has proven to be extremely important in medical imaging. This paper introduces Relational Statistical Deformation Models, or RSDMs, as a generic modeling...
This paper describes a methodology used to classify Alzheimer's disease (AD) and mild cognitive impairment (MCI) with high accuracy using EEG data. The sequential forward floating search (SFFS) was used to select features from relative average power for channel locations in frequency bands delta, theta, alpha, and beta, and coherence between intrahemispheric channel pairs for the same frequency ranges...
The goal of this study was to analyze the magnetoencephalogram (MEG) background activity in patients with Alzheimer's disease (AD) using a regularity measure: approximate entropy (ApEn). This measure was computed for a broad band (0.5-40 Hz) as well as typical frequency bands (delta, theta, alpha, beta and gamma). Five minutes of recording were acquired with a 148-channel whole-head magnetometer in...
This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the calculation of the skewness to each m-by-m sliding block of the transaxial slices of the SPECT brain images. We replace the center pixel in the m-by-m block by the skewness value and build a new 3-D brain image which will...
This paper shows a new computer aided diagnosis (CAD) technique for the early Alzheimer's disease (AD) based on single photon emission computed tomography (SPECT) image feature selection and a statistical learning theory (SLT) classifier. Conventional evaluation of SPECT is time consuming, subjective and prone to error because images often rely on manual reorientation, visual reading of tomographic...
With increasing life expectancy in developed countries, there is a corresponding increase in the frequency of diseases typically associated with old age, in particular dementia. In recent research, multivariate analysis of Positron Emission Tomography (PET) datasets has shown potential for classification between Alzheimer's disease (AD) patients and asymptomatic controls. In this work, the feasibility...
Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. With the growth of the older population in developed nations, the prevalence of AD is expected to triple over the next 50 years while its early diagnosis remains being a difficult task. Functional imaging modalities including singlephoton emission computed tomography...
In this work, we present a learning framework to help early diagnosis of Alzheimer's disease (AD) from magnetic resonance images. Our approach relies on a nearest neighbor (NN) procedure where the similarity measure is obtained via on-line supervised learning. We propose two alternative approaches to learn the similarities between cases. Several experiments on OASIS database establish that, even with...
The purpose of this study was to explore the automatic method of detecting the gray matter loss of Alzheimerpsilas disease (AD) patients with magnetic resonance imaging (MRI). In this paper, voxel-based morphometry (VBM) and support vector machine (SVM )were combined and introduced to diagnose Alzheimer's disease(AD) for clinical applications. Firstly, with the VBM method, 20 features were obtained...
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