Share Email Print
cover

Proceedings Paper

Machine learning for the assessment of Alzheimer's disease through DTI
Author(s): Eufemia Lella; Nicola Amoroso; Roberto Bellotti; Domenico Diacono; Marianna La Rocca; Tommaso Maggipinto; Alfonso Monaco; Sabina Tangaro
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Digital imaging techniques have found several medical applications in the development of computer aided detection systems, especially in neuroimaging. Recent advances in Diffusion Tensor Imaging (DTI) aim to discover biological markers for the early diagnosis of Alzheimer’s disease (AD), one of the most widespread neurodegenerative disorders. We explore here how different supervised classification models provide a robust support to the diagnosis of AD patients. We use DTI measures, assessing the structural integrity of white matter (WM) fiber tracts, to reveal patterns of disrupted brain connectivity. In particular, we provide a voxel-wise measure of fractional anisotropy (FA) and mean diffusivity (MD), thus identifying the regions of the brain mostly affected by neurodegeneration, and then computing intensity features to feed supervised classification algorithms. In particular, we evaluate the accuracy of discrimination of AD patients from healthy controls (HC) with a dataset of 80 subjects (40 HC, 40 AD), from the Alzheimer’s Disease Neurodegenerative Initiative (ADNI). In this study, we compare three state-of-the-art classification models: Random Forests, Naive Bayes and Support Vector Machines (SVMs). We use a repeated five-fold cross validation framework with nested feature selection to perform a fair comparison between these algorithms and evaluate the information content they provide. Results show that AD patterns are well localized within the brain, thus DTI features can support the AD diagnosis.

Paper Details

Date Published: 19 September 2017
PDF: 8 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039619 (19 September 2017); doi: 10.1117/12.2274140
Show Author Affiliations
Eufemia Lella, Univ. degli Studi di Bari (Italy)
Istituto Nazionale di Fisica Nucleare (Italy)
Nicola Amoroso, Univ. degli Studi di Bari (Italy)
Istituto Nazionale di Fisica Nucleare (Italy)
Roberto Bellotti, Univ. degli Studi di Bari (Italy)
Istituto Nazionale di Fisica Nucleare (Italy)
Domenico Diacono, Istituto Nazionale di Fisica Nucleare (Italy)
Marianna La Rocca, Univ. degli Studi di Bari (Italy)
Istituto Nazionale di Fisica Nucleare (Italy)
Tommaso Maggipinto, Univ. degli Studi di Bari (Italy)
Istituto Nazionale di Fisica Nucleare (Italy)
Alfonso Monaco, Istituto Nazionale di Fisica Nucleare (Italy)
Sabina Tangaro, Istituto Nazionale di Fisica Nucleare (Italy)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top