MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

Antonio Martínez -Torteya; Juan Rodríguez-Rojas; José M. Celaya-Padilla; Jorge I. Galván-Tejada; Victor Treviño; José G. Tamez-Peña

doi:10.1117/12.2043903

20 March 2014 MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

Antonio Martínez -Torteya, Juan Rodríguez-Rojas, José M. Celaya-Padilla, Jorge I. Galván-Tejada, Victor Treviño, José G. Tamez-Peña

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903526 (2014) https://doi.org/10.1117/12.2043903
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

An early diagnosis of Alzheimer’s disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer’s Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e^-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

Citation Download Citation

Antonio Martínez -Torteya, Juan Rodríguez-Rojas, José M. Celaya-Padilla, Jorge I. Galván-Tejada, Victor Treviño, and José G. Tamez-Peña "MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903526 (20 March 2014); https://doi.org/10.1117/12.2043903

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available