Edge detection in ventriculograms using support vector machine classifiers and deformable models

Authors:
Antonio Bravo;Miguel Vera;Rubén Medina
Affiliations:
Grupo de Bioingeniería, Universidad Nacional Experimental del Táchira, Decanato de Investigación, San Cristóbal, Venezuela;Laboratorio de Física, Departamento de Ciencias, Universidad de Los Andes-Táchira, San Cristóbal, Venezuela;Grupo de Ingeniería Biomédica, Universidad de Los Andes, Facultad de Ingeniería, Mérida, Venezuela
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

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Abstract

In this paper a left ventricle (LV) contour detection method is described. The method works from an approximate contour defined by anatomical landmarks extracted using Support Vector Machine (SVM) classifiers. The LV contour approximation is used as an initialization step for the deformable model algorithm. An optimization method based on a gradient descend algorithm is used to obtain the optimal contour that provides a minimum energy value. Both classifier and edge detection method performances have been validated. The error is determined as the difference between the shape estimated by the algorithm and the shape traced by an expert.