Automatic view recognition in echocardiogram videos using parts-based representation

Abstract

Indexing echocardiogram videos at different levels of structure is essential for providing efficient access to their content for browsing and retrieval purposes. We present a novel approach for the automatic identification of the views of the heart from the content of the echocardiogram videos. In this approach the structure of the heart is represented by the constellation of its parts (chambers) under the different views. The statistical variations of the parts in the constellation and their spatial relationships are modeled using Markov random field models. A discriminative method is then used for view recognition, which fuses the assessments of a test image, by all the view-models. To the best of our knowledge, this is the first work addressing the analysis of the echocardiogram videos for the purpose of indexing their content. The method presented could be used for multiple-object recognition when their parts represent the objects and there are structural similarities between them.

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