Ricardo Guerrero

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We propose a framework for feature extraction from learned low-dimensional subspaces that represent inter-subject variability. The manifold subspace is built from data-driven regions of interest (ROI). The regions are learned via sparse regression using the mini-mental state examination (MMSE) score as an independent variable which correlates better with(More)
Machine learning techniques have been widely used to detect morphological abnormalities from structural brain magnetic resonance imaging data and to support the diagnosis of neurological diseases such as dementia. In this paper, we propose to use a multiple instance learning (MIL) method in an application for the detection of Alzheimer's disease (AD) and(More)
The identification of anatomical landmarks in medical images is an important task in registration and morphometry. Manual labeling is time consuming and prone to observer errors. We propose a manifold learning procedure, based on Laplacian Eigenmaps, that learns an embedding from patches drawn from multiple brain MR images. The position of the patches in(More)
In this paper, we propose an image registration algorithm named statistically-based FFD registration (SFFD). This registration method is a modification of a well-known free-form deformations (FFD) approach. Our framework dramatically reduces the number of parameters to optimise and only needs to perform a single-resolution optimisation to account for coarse(More)
Different neurodegenerative diseases can cause memory disorders and other cognitive impairments. The early detection and the stratification of patients according to the underlying disease are essential for an efficient approach to this healthcare challenge. This emphasizes the importance of differential diagnostics. Most studies compare patients and(More)
The estimation of disease progression in Alzheimer's disease (AD) based on a vector of quantitative biomarkers is of high interest to clinicians, patients, and biomedical researchers alike. In this work, quantile regression is employed to learn statistical models describing the evolution of such biomarkers. Two separate models are constructed using (1)(More)
OBJECTIVE Identifying mild cognitive impairment (MCI) subjects who will progress to Alzheimer's disease (AD) is not only crucial in clinical practice, but also has a significant potential to enrich clinical trials. The purpose of this study is to develop an effective biomarker for an accurate prediction of MCI-to-AD conversion from magnetic resonance(More)