@misc{10481/46939,
year = {2017},
url = {http://hdl.handle.net/10481/46939},
abstract = {The rise of neuroimaging in the last years has provided physicians and radiologist
with the ability to study the brain with unprecedented ease. This
led to a new biological perspective in the study of neurodegenerative diseases,
allowing the characterization of different anatomical and functional patterns
associated with them. Computer Aided Diagnosis (CAD) systems use statistical
techniques for preparing, processing and extracting information from
neuroimaging data pursuing a major goal: optimize the process of analysis
and diagnosis of neurodegenerative diseases and mental conditions.
With this thesis we focus on three different stages of the CAD pipeline: preprocessing,
feature extraction and validation. For preprocessing, we have
developed a method that target a relatively recent concern: the confounding
effect of false positives due to differences in the acquisition at multiple
sites. Our method can effectively merge datasets while reducing the acquisition
site effects. Regarding feature extraction, we have studied decomposition
algorithms (independent component analysis, factor analysis), texture features
and a complete framework called Spherical Brain Mapping, that reduces the
3-dimensional brain images to two-dimensional statistical maps. This allowed
us to improve the performance of automatic systems for detecting Alzheimer’s
and Parkinson’s diseases. Finally, we developed a brain simulation technique
that can be used to validate new functional datasets as well as for educational
purposes.},
organization = {Tesis Univ. Granada. Programa Oficial de Doctorado en: Tecnologías de la Información y la Comunicación},
organization = {Contrato de investigación asociado al proyecto de excelencia P11-TIC- 7103 de la Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía, Spain).},
organization = {Financiación complementaria de los proyectos P09-TIC-4530 de la Junta de Andalucía y TEC2008-02113, TEC2012-34306 y TEC2015-64718-R del MINECO/ FEDER.},
publisher = {Universidad de Granada},
keywords = {Neurología},
keywords = {Imágenes},
keywords = {Diagnóstico},
keywords = {Algoritmos},
keywords = {Procesado de imágenes},
title = {Statistical neuroimange modeling, processing and syntheris based on texture and component analysis: Tackling the small sample size problem},
author = {Martínez Murcia, Francisco Jesús},
}