3D echocardiography is an imaging modality that enables a more complete and rapid cardiac function assessment. However, as a time-consuming procedure, it calls upon automatic view detection to enable fast 3D volume navigation and analysis. We propose a combinatorial model- and machine learning-based left ventricle (LV) apical view detection method consisting of three steps: first, multiscale local phase-based 3D boundary detection is used to fit a deformable model to the boundaries of the LV blood pool. After candidate slice extraction around the derived mid axis of the LV segmentation, we propose the use of local phase-based Fast Ray features to complement conventional Haar features in an AdaBoost-based framework for automated standardized LV apical view detection. Evaluation performed on a combination of healthy volunteers and clinical patients with different image quality and ultrasound probes show that apical plane views can be accurately identified in a 360 degree swipe of 3D frames. © 2014 Springer International Publishing Switzerland.

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Conference paper

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8331 LNCS


119 - 129