Radiomics Textural Features Extracted from Subcortical Structures of Grey Matter Probability for Alzheimers Disease Detection

• Published in: Proceedings / IEEE International Symposium on Computer-Based Medical Systems pp. 391 - 397
• Main Authors: Ortiz Toro, Cesar Antonio, Gutierrez Sanchez, Nuria, Gonzalo-Martin, Consuelo, Garrido Garcia, Roberto, Rodriguez Gonzalez, Alejandro, Menasalvas Ruiz, Ernestina
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2019
• Subjects: Alzheimer's disease; Feature extraction; Grey matter; Magnetic resonance imaging; Radiomics; Support vector machines
• ISSN: 2372-9198
• DOI: 10.1109/CBMS.2019.00084

Alzheimer's disease (AD) is characterized by a progressive deterioration of cognitive and behavioral functions as a result of the atrophy of specific regions of the brain. It is estimated that by 2050 there will be 131.5 million people affected. Thus, there is an urgent need to find biological markers for its early detection and monitoring. In this work, it is present an analysis of textural radiomics features extracted from a gray matter probability volume, in a set of individual subcortical regions, from a number of different atlases, to identify subject with AD in a MRI. Also, significant subcortical regions for AD detection have been identified using a ReliefF relevance test. Experimental results using the ADNI1 database have proven the potential of some of the tested radiomic features as possible biomarkers for AD/CN differentiation.