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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.

A Clustering Based Approach for Automatic Image Segmentation: An Application to Biplane Ventriculograms