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Diagnostics 2018, 8(3), 47; https://doi.org/10.3390/diagnostics8030047

ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer’s Disease by Means of Textures Analysis on Magnetic Resonance Images

1
Center for Biomaterials and Tissue Engineering, Universitat Politècnica de València, Camí de Vera s/n, 46022 Valencia, Spain
2
Radiology Department, Hospital Universitario de la Ribera, Alzira, 46022 Valencia, Spain
These authors contributed equally to this work.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
*
Author to whom correspondence should be addressed.
Received: 29 May 2018 / Revised: 10 July 2018 / Accepted: 18 July 2018 / Published: 19 July 2018
(This article belongs to the Special Issue Brain Imaging)
Full-Text   |   PDF [2678 KB, uploaded 19 July 2018]   |  

Abstract

The current criteria for diagnosing Alzheimer’s disease (AD) require the presence of relevant cognitive deficits, so the underlying neuropathological damage is important by the time the diagnosis is made. Therefore, the evaluation of new biomarkers to detect AD in its early stages has become one of the main research focuses. The purpose of the present study was to evaluate a set of texture parameters as potential biomarkers of the disease. To this end, the ALTEA (ALzheimer TExture Analyzer) software tool was created to perform 2D and 3D texture analysis on magnetic resonance images. This intuitive tool was used to analyze textures of circular and spherical regions situated in the right and left hippocampi of a cohort of 105 patients: 35 AD patients, 35 patients with early mild cognitive impairment (EMCI) and 35 cognitively normal (CN) subjects. A total of 25 statistical texture parameters derived from the histogram, the Gray-Level Co-occurrence Matrix and the Gray-Level Run-Length Matrix, were extracted from each region and analyzed statistically to study their predictive capacity. Several textural parameters were statistically significant (p < 0.05) when differentiating AD subjects from CN and EMCI patients, which indicates that texture analysis could help to identify the presence of AD. View Full-Text
Keywords: Alzheimer’s disease; mild cognitive impairment; software; biomarkers; texture analysis; magnetic resonance imaging Alzheimer’s disease; mild cognitive impairment; software; biomarkers; texture analysis; magnetic resonance imaging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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López-Gómez, C.; Ortiz-Ramón, R.; Mollá-Olmos, E.; Moratal, D.; for the Alzheimer’s Disease Neuroimaging Initiative. ALTEA: A Software Tool for the Evaluation of New Biomarkers for Alzheimer’s Disease by Means of Textures Analysis on Magnetic Resonance Images. Diagnostics 2018, 8, 47.

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