RT Journal Article
T1 Discriminative Sparse Features for Alzheimer’s Disease Diagnosis using multimodal image data.
A1 Ortiz-García, Andrés
A1 Lozano Cuadra, Federico
A1 Górriz-Sáez, Juan Manuel
A1 Ramírez-Aguilar, Francisco Javier
A1 Martínez-Murcia, Francisco Jesús
K1 Alzheimer, Enfermedad de - Diagnóstico por imagen -  Proceso de datos
K1 Diagnóstico - Proceso de datos
K1 Medicina - Proceso de datos
AB Feature extraction in medical image processing still remains a challenge, especially in high-dimensionality datasets, where the expected number of availablesamples is considerably lower than the dimension of the feature space. This is acommon problem in real-world data, and, specifically, in medical image processing as, while images are composed of hundreds of thousands voxels, only areduced number of patients are available. Extracting descriptive and discriminative features allows representing each sample by a small number of features,which is particularly important in classification task, due to the curse of dimensionality problem. In this paper we solve this recognition problem by means ofsparse representations of the data, which also provides an arena to multimodalimage (PET and MRI) data classification by combining specialized classifiers.Thus, a novel method to effectively combine SVC classifiers is presented here,which uses the distance to the hyperplane computed for each class in each classifier allowing to select the most discriminative image modality in each case. Thediscriminative power of each modality also provides information about the illnessevolution; while functional changes are clearly found in Alzheimer’s diagnosedpatients (AD) when compared to control subjects (CN), structural changes seem tobe more relevant at the early stages of the illness, affecting Mild Cognitive Impairment (MCI) patients. Finally, classification experiments using 68 CN, 70 ADand 111 MCI images and assessed by cross-validation show the effectiveness ofthe proposed method. Accuracy values of up to 92% and 79% for CN/AD andCN/MCI classification are achieved.
PB Bentham Science
YR 2018
FD 2018-01-01
LK https://hdl.handle.net/10630/28093
UL https://hdl.handle.net/10630/28093
LA eng
NO Ortiz A, Lozano F, Gorriz JM, Ramirez J, Martinez Murcia FJ; Alzheimer's Disease Neuroimaging Initiative. Discriminative Sparse Features for Alzheimer's Disease Diagnosis Using Multimodal Image Data. Curr Alzheimer Res. 2018;15(1):67-79. doi: 10.2174/1567205014666170922101135. PMID: 28934923.
NO This work was partly supported by the MINECO/FEDER under TEC2015-64718-R and PSI2015-65848-R projects and the Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under theExcellence Project P11-TIC-7103. Data collection and sharing for this project was funded by theAlzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904)and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by theNational Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, andthrough generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s DrugDiscovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company;CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F.Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICOLtd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & JohnsonPharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso ScaleDiagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation;Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. TheCanadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada.Private sector contributions are facilitated by the Foundation for the National Institutes of Health(www.fnih.org). The grantee organization is the Northern California Institute for Research and Education,and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University ofSouthern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the Universityof Southern California.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026