Ben A. Lin

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This paper presents a dynamical appearance model based on sparse representation and dictionary learning for tracking both endocardial and epicardial contours of the left ventricle in echocardiographic sequences. Instead of learning offline spatiotemporal priors from databases, we exploit the inherent spatiotemporal coherence of individual data to constraint(More)
The spatio-temporal coherence in data plays an important role in echocardiographic segmentation. While learning offline dynamical priors from databases has received considerable attention, these priors may not be suitable for post-infarct patients and children with congenital heart disease. This paper presents a dynamical appearance model (DAM) driven by(More)
Quantitative analysis of left ventricular deformation can provide valuable information about the extent of disease as well as the efficacy of treatment. In this work, we develop an adaptive multi-level compactly supported radial basis approach for deformation analysis in 3D+time echocardiography. Our method combines displacement information from shape(More)
We have applied a novel high temporal resolution MR imaging sequence to study diastolic function in canines with reperfused transmural infarction. Our results demonstrate abnormal diastolic strain-rates in infarct and viable risk region with corresponding abnormal filling patterns, as observed through the visualization of 2D flow pathlines 3 days post(More)
Dictionary learning has been shown to be effective in exploiting spatiotemporal coherence for echocardiographic segmentation. To overcome the limitations of previous methods, we present a stochastic online dictionary learning approach for segmenting left ventricular borders from 4D echocardiography. It is based on stochastic approximations and processes a(More)