Automatic computation of regions of interest by robust principal component analysis. Application to automatic dementia diagnosis

Computer aided diagnosis systems based on brain imaging are a powerful tool to assist in the diagnosis of Alzheimer’s Disease (AD). The goal is the automatic recognizing of neurodegenerative patterns that characterize the disease. In this regard, determining regions related to the disease results cr...

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Vydáno v:Knowledge-based systems Ročník 123; s. 229 - 237
Hlavní autoři: Lozano, Francisco, Ortiz, Andrés, Munilla, Jorge, Peinado, Alberto, Initiative, for the Alzheimer’s Disease Neuroimaging
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.05.2017
Elsevier Science Ltd
Témata:
Algorithms
Alzheimer's disease
Automatic
Brain
CAD
Classification
Computation
Computer aided design
Computer-aided diagnosis
Dementia
Diagnosis
Diagnostic systems
Disease
Experiments
Exploratory analysis
Image classification
Information fusion
Machine learning
Magnetic resonance
Medical diagnosis
Multimodal image data
Multimodality
Neurodegeneration
Neuroimaging
Patients
Pattern recognition
Positron emission
Principal components analysis
Regions
Regions of interest
Research subjects
Robust PCA
Tomography
Training
Robust PCA
Exploratory analysis
Alzheimer’s disease
Information fusion
Multimodal image data
Regions of interest
Computer-aided diagnosis
ISSN:0950-7051, 1872-7409
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Shrnutí:Computer aided diagnosis systems based on brain imaging are a powerful tool to assist in the diagnosis of Alzheimer’s Disease (AD). The goal is the automatic recognizing of neurodegenerative patterns that characterize the disease. In this regard, determining regions related to the disease results crucial to select the most discriminative voxels and to optimize the number of features to be used in the learning algorithm. In this paper, we propose a method based on the robust principal component analysis (Robust PCA) algorithm that allows to automatically compute Regions Of Interest (ROIs) over a training set of images and rank them according to their diagnostic relevance. Robust PCA is used to compute the sparse error matrix, which is, in turn, employed to determine the brain areas related to the Alzheimer’s disease. These areas are further used as a mask to select and weight the most discriminative voxels to construct a classification model. We then describe a method to fuse the features computed from different image modalities based on the weights assigned by the individual Support Vector Classifiers during the training process. The method presented here has been applied to multimodal image containing both functional (18F-FDG PET) and structural (Magnetic Resonance) data. Experiments, conducted using 68 control subjects and 70 CE patients, show the effectiveness of the proposed approach for the exploratory analysis. At the same time, classification experiments using the features computed by the proposed method and assessed by cross-validation showed accuracy values up to 92% and AUC (Area Under the Curve) of 0.95. Thus, the proposal seems as an effective technique to reveal ROIs in differential diagnosis applications and to combine multimodal image data, outperforming other classification methods, including the voxel-as-features (VAF) baseline.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2017.02.025