The fuzzy local-global duality in detecting pictorial patterns
Pattern Recognition Letters
Finding salient regions in images: nonparametric clustering for image segmentation and grouping
Computer Vision and Image Understanding - Special issue on content-based access for image and video libraries
Quantitative analysis of cardiac function
Handbook of medical imaging
Computer and Robot Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
FIMH'05 Proceedings of the Third international conference on Functional Imaging and Modeling of the Heart
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Edge detection in ventriculograms using support vector machine classifiers and deformable models
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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This paper reports on an automatic method for ventricular cavity segmentation in angiographic images. The first step of the method consists in applying a linear regression model that exploits the functional relationship between the original input image and a smoothed version. This intermediate result is used as input to a clustering algorithm, which is based on a region growing technique. The clustering algorithm is a two stage process. In the first stage an initial segmentation is achieved using as input the result of the linear regression and the smoothed version of the input image. The second stage is intended for refining the initial segmentation based on feature vectors including the area, the gray-level average and the centroid of each candidate region. The segmentation method is conceptually simple and provides an accurate contour detection for the left ventricle cavity.