Antonio Bravo, José Clemente, Rubén Medina


In this paper an image segmentation scheme that is based on combinations of a non–parametric technique and a seed based clustering algorithm is reported. The method has been applied to clinical unsubtracted angiograms of the human heart. The first step of the method consists in applying a mean shift–based filter in order to improve the left ventricle cavity information in angiographic images. Second, the initial seed is semi–automatically generated from the aortic valve manual localization by a specialist. Third, each angiographic image is segmented using a clustering algorithm that begins with the seed which is grown until image pixels associated to the left ventricle cavity are clustered. A validation is performed by comparing the estimated contours with respect to contours manually traced by a cardiologists. From this validation stage the maximum of the average contour error considering six angiographic sequences (a total of 178 images) is 7.30 % .


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Paper Citation

in Harvard Style

Bravo A., Clemente J. and Medina R. (2010). SEED–GROWING HEART SEGMENTATION IN HUMAN ANGIOGRAMS . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 91-96. DOI: 10.5220/0002848900910096

in Bibtex Style

author={Antonio Bravo and José Clemente and Rubén Medina},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},

in EndNote Style

JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
SN - 978-989-674-029-0
AU - Bravo A.
AU - Clemente J.
AU - Medina R.
PY - 2010
SP - 91
EP - 96
DO - 10.5220/0002848900910096