Computational anatomy is a subdiscipline of the anatomy that studies macroscopic details of the human body structure using a set of automatic techniques. Different reference systems have been developed for brain mapping and morphometry in functional and structural studies. Several models integrate particular anatomical regions to highlight pathological patterns in structural brain MRI, a really challenging task due to the complexity, variability, and nonlinearity of the human brain anatomy. In this paper, we present a strategy that aims to find anatomical regions with pathological meaning by using a probabilistic analysis. Our method starts by extracting visual primitives from brain MRI that are partitioned into small patches and which are then softly clustered, forming different regions not necessarily connected. Each of these regions is described by a co- occurrence histogram of visual features, upon which a probabilistic semantic analysis is used to find the underlying structure of the information, i.e., separated regions by their low level similarity. The proposed approach was tested with the OASIS data set which includes 69 Alzheimer’s disease (AD) patients and 65 healthy subjects (NC).
In structural Magnetic Resonance Imaging (MRI), neurodegenerative diseases generally present complex brain patterns that can be correlated with di erent clinical onsets of this pathologies. An objective method that aims to determine both global and local changes is not usually available in clinical practice, thus the interpretation of these images is strongly dependent on the radiologist's skills. In this paper, we propose a strategy which interprets the brain structure using a framework that highlights discriminant brain patterns for neurodegenerative diseases. This is accomplished by combining a probabilistic learning technique, which identi es and groups regions with similar visual features, with a visual saliency method that exposes relevant information within each region. The association of such patterns with a speci c disease is herein evaluated in a classi cation task, using a dataset including 80 Alzheimer's disease (AD) patients and 76 healthy subjects (NC). Preliminary results show that the proposed method reaches a maximum classi cation accuracy of 81.39%.
Accurate diagnosis of Alzheimer's disease (AD) from structural Magnetic Resonance (MR) images is difficult due to the complex alteration of patterns in brain anatomy that could indicate the presence or absence of the pathology. Currently, an effective approach that allows to interpret the disease in terms of global and local changes is not available in the clinical practice. In this paper, we propose an approach for classification of brain MR images, based on finding pathology-related patterns through the identification of regional structural changes. The approach combines a probabilistic Latent Semantic Analysis (pLSA) technique, which allows to identify image regions through latent topics inferred from the brain MR slices, with a bottom-up Graph-Based Visual Saliency (GBVS) model, which calculates maps of relevant information per region. Regional saliency maps are finally combined into a single map on each slice, obtaining a master saliency map of each brain volume. The proposed approach includes a one-to-one comparison of the saliency maps which feeds a Support Vector Machine (SVM) classifier, to group test subjects into normal or probable AD subjects. A set of 156 brain MR images from healthy (76) and pathological (80) subjects, splitted into a training set (10 non-demented and 10 demented subjects) and one testing set (136 subjects), was used to evaluate the performance of the proposed approach. Preliminary results show that the proposed method reaches a maximum classification accuracy of 87.21%.
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