Functional Brain Image Classification Techniques for Early Alzheimer Disease Diagnosis
ABSTRACT Currently, the accurate diagnosis of the Alzheimer disease (AD) still remains a challenge in the clinical practice. As the
number of AD patients has increased, its early diagnosis has received more attention for both social and medical reasons.
Single photon emission computed tomography (SPECT), measuring the regional cerebral blood flow, enables the diagnosis even
before anatomic alterations can be observed by other imaging techniques. However, conventional evaluation of SPECT images
often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. This paper
evaluates different pattern classifiers including k-nearest neighbor (kNN), classification trees, support vector machines and feedforward neural networks in combination with template-based normalized
mean square error (NMSE) features of several coronal slices of interest (SOI) for the development of a computer aided diagnosis
(CAD) system for improving the early detection of the AD. The proposed system, yielding a 98.7% AD diagnosis accuracy, reports
clear improvements over existing techniques such as the voxel-as-features (VAF) which yields just a 78% classification accuracy.
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ABSTRACT: Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. However, currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician’s diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on feature selection and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach attaining a 90% AD diagnosis accuracy.07/2009: pages 410-417;
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ABSTRACT: Alzheimer disease (AD), a progressive neurodegenerative disorder, is the most common cause of dementia in the elderly. Current consensus statements have emphasized the need for early recognition and the fact that a diagnosis of AD can be made with high accuracy by using clinical, neuropsychologic, and imaging assessments. Magnetic resonance (MR) or computed tomographic (CT) imaging is recommended for the routine evaluation of AD. Coronal MR images can be useful to document or quantify atrophy of the hippocampus and entorhinal cortex, both of which occur early in the disease process. Both volumetric and subtraction MR techniques can be used to quantify and monitor dementia progression and rates of regional atrophy. MR measures are also increasingly being used to monitor treatment effects in clinical trials of cognitive enhancers and antidementia agents. Positron emission tomography (PET) and single photon emission CT offer value in the differential diagnosis of AD from other cortical and subcortical dementias and may also offer prognostic value. In addition, PET studies have demonstrated that subtle abnormalities may be apparent in the prodromal stages of AD and in subjects who carry susceptibility genes. PET ligands are in late-stage development for demonstration of amyloid plaques, and human studies have already begun. Functional MR-based memory challenge tests are in development as well.Radiology 03/2003; 226(2):315-36. · 6.34 Impact Factor
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ABSTRACT: This wo rk aims atpro vidi g ato o lto assist the i terpretatio o SPECT images fo the diag ogno Alzheimer's Disease (AD).03/2002;