Paper Info
Title
Independent component analysis-based multimodal classification of Alzheimer's disease versus healthy controls
Abstract
Effective and accurate detection of Alzheimer's disease (AD) at its early stage has been being paid more and more attention. However, most of existing researches have just focused on a single modality of imaging for diagnosis of AD. Although A single modality might be provides some meaningful information for diagnosing the disease, it still is difficult to meet the need of clinical diagnosis. With the fast development of multi-modality imaging such as structural magnetic resonance imaging (sMRI) and positron emission tomography (PET), combination of multi-modality has an opportunity to provide much more useful information to enhance the better performance of diagnosis of AD than that of a single one alone. The integration of the complementary information afforded by multimodal imaging protocols into a comprehensive analysis strategy is likely to aid in better discrimination and staging of AD. In the study, we proposed a method based on independent component analysis and support vector machine by combining sMRI and PET images to carry out the classification of AD subjects from healthy controls (HC). The experimental results illustrated that the classification between AD and HC subjects from the Alzheimer's Disease Neuroimaging Initiative database was obtained with the averaged accuracy of 96.53% for Multimodal images at baseline, comparing to 88.95% for only sMRI images and 89.44% for only PET images. The multimodal classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.
Year
DOI
Venue
2013
10.1109/ICNC.2013.6817947
ICNC
Keywords
Field
DocType
brain abnormality detection,alzheimer's disease neuroimaging initiative database,ad detection,diseases,ad diagnosis performance enhancement,structural magnetic resonance imaging,alzheimer's disease detection,multimodal imaging protocols,complementary information integration,independent component analysis,smri images,pet images,hc,image classification,support vector machine,biomedical mri,independent component analysis-based multimodal classification,positron emission tomography,comprehensive analysis strategy,healthy controls,patient diagnosis,support vector machines,medical image processing,accuracy,magnetic resonance imaging
Disease,Structural magnetic resonance imaging,Computer science,Support vector machine,Positron emission tomography,Artificial intelligence,Independent component analysis,Clinical diagnosis,Neuroimaging,Stage (cooking),Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Jie Guan12212.84
Wenlu Yang2287.81