Abstract
Cardiovascular diseases are the leading cause of death globally. Therefore, classification tools play a major role in prevention and treatment of these diseases. Statistical learning theory applied to magnetic resonance imaging has led to the diagnosis of a variety of cardiomyopathies states. We propose a two-stage classification scheme capable of distinguishing between heterogeneous groups of hypertrophic cardiomyopathies and healthy patients. A multimodal processing pipeline is employed to estimate robust tensorial descriptors of myocardial mechanical properties for both short-axis and long-axis magnetic resonance tagged images using the least absolute deviation method. A homomorphic filtering procedure is used to align the cine segmentations to the tagged sequence and provides 3D tensor information in meaningful areas. Results have shown that the proposed pipeline provides tensorial measurements on which classifiers for the study of hypertrophic cardiomyopathies can be built with acceptable performance even for reduced samples sets.
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Acknowledgments
This work was partially supported by the Spanish Ministerio de Ciencia e Innovacion under Research Grant TEC2013-44194-P, the Spanish Ministerio de Ciencia e Innovacion and the European Regional Development Fund (ERDF-FEDER) under Research Grant TEC2014-57428-R and the Spanish Junta de Castilla y Leon under Grant VA136U13.
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Sanz-Estébanez, S. et al. (2016). An Automated Tensorial Classification Procedure for Left Ventricular Hypertrophic Cardiomyopathy. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_17
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DOI: https://doi.org/10.1007/978-3-319-31744-1_17
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