Publication Type:

Conference Paper

Source:

Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017, Institute of Electrical and Electronics Engineers Inc. (2017)

ISBN:

9781509038008

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046640041&doi=10.1109%2fICCSP.2017.8286577&partnerID=40&md5=62e416cefeff004c3e65b38eab3840ed

Keywords:

Alzheimer's disease, Classification (of information), Computed-aided diagnosis, Diagnosis, Feature extraction, Gaussian Mixture Model, Health consequences, Image segmentation, Least squares approximations, Magnetic Resonance Imaging, Multivariate approach, Neurodegenerative, Neurodegenerative diseases, Partial least square (PLS), Patient treatment, Signal processing, Small sample size problems, Support vector machines

Abstract:

Alzheimer's disease is one of the neurodegenerative dementia which results in dramatic health consequences and has socio-economic implications. Early and accurate diagnosis of Alzheimer's disease is necessary to provide proper treatment to the patients. Recently, several Computed-Aided-Diagnosis systems has been developed for Alzheimer's disease detection from MR images. Although univariate approaches are most widely used, recent investigations focuses on multivariate approaches which deals with whole image as one observation. In the proposed method, detection is done by efficiently segmenting the gray matter region using Gaussian Mixture Models and then extracting the score vectors based on Partial Least Squares. This relieves the small sample size problem and then classification is done by Support Vector Machine, a supervised learning mechanism. The efficiency of the proposed method is compared with existing methods and is found superior. © 2017 IEEE.

Cite this Research Publication

Jyothisha J. Nair and Mohan, N., “Alzheimer's disease diagnosis in MR images using statistical methods”, in Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017, 2017.