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Segmentation and motion estimation applied to fetal heart analysis using a multi-texture active appearance model and an optical flow approach
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Paper Abstract

In this work we present a combination of segmentation and motion estimation methods applied to left ventricle evaluation in fetal echocardiographic images which are used for prenatal diagnosis. In our proposed scheme, several features of the ultrasound images are computed and used for both algorithms. A multiresolution framework is employed for the segmentation and motion estimation tasks. The segmentation is achieved using a multi-texture active appearance model based on the Hermite transform. The analysis is performed using the appearance models provided by Hermite coefficients up to third order. The multiresolution approach allows to obtain a robust segmentation to extract the shape of the left ventricle. The obtained results in the segmentation step are used for the motion estimation algorithm. The left ventricle is the structure used for evaluation. The main goal is to determinate the heart movement of fetal heart which can be used for disease detection, characterization and further analysis. Results of the motion estimation process are analyzed and compared with other techniques applied to heart ultrasound data.

Paper Details

Date Published: 21 December 2018
PDF: 9 pages
Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 1097513 (21 December 2018); doi: 10.1117/12.2512142
Show Author Affiliations
Lorena Vargas-Quintero, Univ. Popular del Cesar (Colombia)
Ernesto Moya-Albor, Univ. Panamericana (Mexico)
Boris Escalante-Ramírez, Univ. Nacional Autónoma de México (Mexico)
Lisbeth Camargo Marín, Instituto Nacional de Perinatología (Mexico)
Mario Guzmán Huerta, Instituto Nacional de Perinatología (Mexico)
Jorge Brieva, Univ. Panamericana (Mexico)


Published in SPIE Proceedings Vol. 10975:
14th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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