Magnetic resonance brain tissue segmentation based on sparse representations

Andrea Rueda

doi:10.1117/12.2207923

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22 December 2015 Magnetic resonance brain tissue segmentation based on sparse representations

Andrea Rueda

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Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 96810G (2015) https://doi.org/10.1117/12.2207923
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador

Abstract

Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).

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Andrea Rueda "Magnetic resonance brain tissue segmentation based on sparse representations", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810G (22 December 2015); https://doi.org/10.1117/12.2207923

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