Heart Cavity Segmentation in Ultrasound Images Based on Supervised Neural Networks

Authors:
Marco Mora;Julio Leiva;Mauricio Olivares
Affiliations:
Department of Computer Science, Catholic University of Maule, Talca, Chile;Department of Mathematics, Catholic University of Maule, Talca, Chile;Department of Computer Science, Catholic University of Maule, Talca, Chile
Venue:
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Year:
2009

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Abstract

This paper proposes a segmentation method of heart cavities based on neural networks. Firstly, the ultrasound image is simplified with a homogeneity measure based on the variance. Secondly, the simplified image is classified using a multilayer perceptron trained to produce an adequate generalization. Thirdly, results from classification are improved by using simple image processing techniques. The method makes it possible to detect the edges of cavities in an image sequence, selecting data for network training from a single image of the sequence. Besides, our proposal permits detection of cavity contours with techniques of a low computational cost, in a robust and accurate way, with a high degree of autonomy.