Alzheimer’s disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex.
Computerized Medical Imaging and Graphics. 2015-09-01; 44: 13-25
DOI: 10.1016/j.compmedimag.2015.04.007
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1. Comput Med Imaging Graph. 2015 Sep;44:13-25. doi:
10.1016/j.compmedimag.2015.04.007. Epub 2015 May 19.
Alzheimer’s disease diagnosis on structural MR images using circular harmonic
functions descriptors on hippocampus and posterior cingulate cortex.
Ben Ahmed O(1), Mizotin M(2), Benois-Pineau J(3), Allard M(4), Catheline G(5),
Ben Amar C(6); Alzheimer’s Disease Neuroimaging Initiative.
Author information:
(1)University of Bordeaux, Laboratoire Bordelais de Recherche en Informatique
(LaBRI), France. Electronic address: .
(2)Lomonosov Moscow State University, Department of Computational Mathematics and
Cybernetics, Moscow, Russia. Electronic address: .
(3)University of Bordeaux, Laboratoire Bordelais de Recherche en Informatique
(LaBRI), France. Electronic address: .
(4)University of Bordeaux, INCIA, UMR 5287, F-33400 Talence, France; CNRS, INCIA,
UMR 5287, F-33400 Talence, France; EPHE, Bordeaux, France. Electronic address:
.
(5)University of Bordeaux, INCIA, UMR 5287, F-33400 Talence, France; CNRS, INCIA,
UMR 5287, F-33400 Talence, France; EPHE, Bordeaux, France. Electronic address:
.
(6)University of Sfax, Research Groups on Intelligent Machine (ReGIM), Tunisia.
Electronic address: .
Recently, several pattern recognition methods have been proposed to automatically
discriminate between patients with and without Alzheimer’s disease using
different imaging modalities: sMRI, fMRI, PET and SPECT. Classical approaches in
visual information retrieval have been successfully used for analysis of
structural MRI brain images. In this paper, we use the visual indexing framework
and pattern recognition analysis based on structural MRI data to discriminate
three classes of subjects: normal controls (NC), mild cognitive impairment (MCI)
and Alzheimer’s disease (AD). The approach uses the circular harmonic functions
(CHFs) to extract local features from the most involved areas in the disease:
hippocampus and posterior cingulate cortex (PCC) in each slice in all three brain
projections. The features are quantized using the Bag-of-Visual-Words approach to
build one signature by brain (subject). This yields a transformation of a full 3D
image of brain ROIs into a 1D signature, a histogram of quantized features. To
reduce the dimensionality of the signature, we use the PCA technique. Support
vector machines classifiers are then applied to classify groups. The experiments
were conducted on a subset of ADNI dataset and applied to the « Bordeaux-3City »
dataset. The results showed that our approach achieves respectively for ADNI
dataset and « Bordeaux-3City » dataset; for AD vs NC classification, an accuracy of
83.77% and 78%, a specificity of 88.2% and 80.4% and a sensitivity of 79.09% and
74.7%. For NC vs MCI classification we achieved for the ADNI datasets an accuracy
of 69.45%, a specificity of 74.8% and a sensitivity of 62.52%. For the most
challenging classification task (AD vs MCI), we reached an accuracy of 62.07%, a
specificity of 75.15% and a sensitivity of 49.02%. The use of PCC visual features
description improves classification results by more than 5% compared to the use
of hippocampus features only. Our approach is automatic, less time-consuming and
does not require the intervention of the clinician during the disease diagnosis.
Copyright © 2015 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.compmedimag.2015.04.007
PMID: 26069906 [Indexed for MEDLINE]