@inproceedings{fb9cc57a0bf4487e8ac4e48a05ab2a29,
title = "Seed-growing heart segmentation in human angiograms",
abstract = "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 %.",
keywords = "Cardiac images, Human heart, Left ventricle, Mean shift, Segmentation, Unsupervised clustering",
author = "Antonio Bravo and Jos{\'e} Clemente and Rub{\'e}n Medina",
year = "2010",
language = "English",
isbn = "9789896740283",
series = "VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications",
pages = "91--96",
booktitle = "VISAPP 2010 - Proceedings of the International Conference on Computer Vision Theory and Applications",
note = "5th International Conference on Computer Vision Theory and Applications, VISAPP 2010 ; Conference date: 17-05-2010 Through 21-05-2010",
}