RT Dissertation/Thesis
T1 Multivariate and sparse signal processing techniques in multimodal neuroimage analysis for the identification of neurological alterations.
A1 Lozano Gómez, Francisco
K1 Alzheimer, Enfermedad de - Diagnóstico - Tesis doctorales
K1 Diagnóstico asistido por ordenador - Tesis doctorales
AB The diagnosis of neurodegenerative diseases, particularly Alzheimer’s Dis-ease (AD) and Parkinsonian Syndrome (PS), has been significantly enhanced bythe advent of Computer Aided Diagnosis (CAD) systems. These systems, lever-aging advanced computational methodologies, aim to automate the recogni-tion of neurodegenerative patterns characteristic of these diseases. This disser-tation presents a series of innovative methodologies that have been developedto address the challenges and nuances of medical image processing in thecontext of these diseases.A cornerstone of this research is the application of Robust Principal Compo-nent Analysis (RPCA) to brain imaging. This technique facilitates the automaticcomputation of Regions of Interest (ROIs) in brain images, ranking them basedon their diagnostic relevance. The sparse error matrix, derived from RPCA, hasemerged as a pivotal tool in determining brain areas intrinsically linked to AD.Furthermore, the fusion of features from diverse image modalities, such asfunctional Positron Emission Tomography (PET) and structural Magnetic Res-onance Imaging (MRI) data, has been explored, yielding promising results inboth exploratory analysis and classification tasks.The challenge of feature extraction, especially in high-dimensionality data-sets, remains a significant hurdle in medical image processing. This researchaddresses this challenge through sparse representations of data, offering asolution to the curse of dimensionality. By combining specialized classifiers,this approach not only aids in classification but also provides insights into theprogression of illnesses. Notably, while functional changes are evident in ADpatients, structural alterations become more pronounced during the disease’searly stages.
PB UMA Editorial
YR 2024
FD 2024
LK https://hdl.handle.net/10630/31126
UL https://hdl.handle.net/10630/31126
LA eng
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026