Authors:
Miguel A. Vera
1
and
Antonio J. Bravo
2
Affiliations:
1
Universidad de Los Andes–Táchira, Venezuela
;
2
Universidad Nacional Experimental del Táchira, Venezuela
Keyword(s):
Human heart, anatomical landmarks, left ventricle, patterns classification, support vectors machines.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Feature Extraction
;
Features Extraction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Medical Image Analysis
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
This paper introduces an approach for efficient myocardial landmarks detection in angiograms. Several anatomical landmarks located on the left ventricle are obtained by mean of a support vector machine. Training set corresponds a dataset of landmark and non-landmark 31×31 pixel patterns. Our support vector machine uses the structural risk minimization principle as inference rule and radial basis function kernel. In the training phase false positives were not registered and in the detection phase 100% of recognition was obtained.