RT Journal Article
T1 Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.
A1 Ortiz-García, Andrés
A1 Munilla-Fajardo, Jorge
A1 Martínez-Murcia, Francisco Jesús
A1 Górriz-Sáez, Juan Manuel
A1 Ramírez-Aguilar, Francisco Javier
K1 Alzheimer, Enfermedad de
K1 Parkinson, Enfermedad de
K1 Diagnóstico por imagen
AB Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.
PB World Scientific
YR 2018
FD 2018-10-16
LK https://hdl.handle.net/10630/28104
UL https://hdl.handle.net/10630/28104
LA eng
NO Ortiz, Andrés & Munilla, Jorge & Martínez-Murcia, Francisco & Gorriz, Juan & Ramírez, Javier. (2018). Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling. International Journal of Neural Systems. 29. 10.1142/S0129065718500405.
NO This work was partly supported by the MINECO/FEDER under TEC2015-64718-R and PSI2015-65848-R projects and the Consejer´ıa de Innovaci´on,Ciencia y Empresa (Junta de Andaluc´ıa, Spain)under the Excellence Project P11-TIC-7103 as wellas the Salvador deMadariaga Mobility Grants 2017.Data collection and sharing for this project wasfunded by the ADNI (National Institutes of HealthGrant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute onAging, the National Institute of Biomedical Imagingand Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation;Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.;Elan Pharmaceuticals, Inc.; Eli Lilly and Company;EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson &Johnson Pharmaceutical Research & DevelopmentLLC.; Lumosity; Lundbeck; Merck & Co., Inc.;Meso Scale Diagnostics, LLC.; NeuroRx Research;Neurotrack Technologies; Novartis PharmaceuticalsCorporation; P zer Inc.; Piramal Imaging; Servier;Takeda Pharmaceutical Company; and TransitionTherapeutics. The Canadian Institutes of HealthResearch is providing funds to support ADNI clin ical sites in Canada. Private sector contributionsare facilitated by the Foundation for the NationalInstitutes of Health (www.fnih.org). The granteeorganization is the Northern California Institute forResearch and Education, and the study is coor dinated by the Alzheimer’s Disease CooperativeStudy at the University of California, San Diego.ADNI data are disseminated by the Laboratory forNeuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is fundedby the Michael J. Fox Foundation for Parkinson’sResearch and funding partners, including [list the fullnames of all of the PPMI funding partners found atwww.ppmi-info.org/fundingpartners].
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026