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3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized...
We propose a fully automatic system for cardiac view classification of echocardiogram. Given an echo study video sequence, the system outputs a view label among the pre-defined standard views. The system is built based on a machine learning approach that extracts knowledge from an annotated database. It characterizes three features: 1) integrating local and global evidence, 2) utilizing view specific...
We propose a probabilistic, hierarchical, and discriminant (PHD) framework for fast and accurate detection of deformable anatomic structures from medical images. The PHD framework has three characteristics. First, it integrates distinctive primitives of the anatomic structures at global, segmental, and landmark levels in a probabilistic manner. Second, since the configuration of the anatomic structures...
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