Classification of Alzheimer's Disease in MRI based on Dictionary Learning and Heavy Tailed Modelling.

Diagnosis of brain diseases is considered one of the most challenging medical tasks to perform, even for medical experts who rely on high-resolution anatomical images to identify signs of abnormalities by visual inspection. However, new computational tools which assist to automate this diagnosis have the potential to significantly improve the speed and accuracy of this process. This work presents a model to aid in the task of classification of structural Magnetic Resonance Imaging scans. The classification is performed using a Support Vector Machine, whilst the features to analyze belong to a dictionary space. Such space was mainly built from a dictionary learning perspective, although a predefined one was also assessed. The results indicate that features learnt from the data of interest lead to improved classification performance. The proposed framework was tested on the ADNI dataset stage I.

Super-Resolution OCT Using Sparse Representations and Heavy-Tailed Models.

This paper introduces a new approach to single-image super-resolution in Optical Coherence Tomography (OCT) images. Retinal OCT images can be used to diagnose various diseases, not only peculiar to the eye, but also some systemic diseases. Nevertheless, as with any imaging modality, the acquired images suffer from degradation due to various causes. To overcome this and enhance image quality, Super-Resolution (SR) techniques are widely used. This work explores a convex regularization approach based on a multivariate generalization of the minimax-concave (GMC) scheme in a forward-backward splitting (FBS) scheme. Based on the assumption that sparse representations of OCT images are heavy-tailed, an α-stable dictionary is employed. This approach is implemented with overlapping and non-overlapping patches. Since the Point Spread Function (PSF) of the images used is generally unknown, it is estimated using a method originally proposed for ultrasound images. The algorithm is tested on OCT images of murine eyes. The results show that the proposed convex regularization method provides results that are competitive with the state-of-the-art. Indeed, significant deblurring and quality enhancement are achieved using the proposed algorithm and in most cases it provides the best results, both objectively and subjectively.