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28 January 2015 Discovering anatomical patterns with pathological meaning by clustering of visual primitives in structural brain MRI
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Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928705 (2015) https://doi.org/10.1117/12.2073873
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia
Abstract
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).
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Juan Leon, Andrea Pulido, and Eduardo Romero "Discovering anatomical patterns with pathological meaning by clustering of visual primitives in structural brain MRI", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928705 (28 January 2015); https://doi.org/10.1117/12.2073873
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