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Statistical modeling of high dimensional, correlated, and complex imaging data obtained from various longitudinal neuroimaging studies has become an inevitable part of automatic disease diagnosing tasks. In this paper, we propose a parametric brain image modeling based on the statistical properties of non-subsampled shearlet transform (NSST) coefficients. The NSST detail coefficients of multimodal...
The problem of estimating the exact shape of the glottal flow derivative (GFD) using reweighted 1-norm minimization of the second derivative of the GFD is addressed in this paper. By using physiological models of the glottal flow derivative, such as the Liljencrants-Fant (LF) and Rosenberg models, it is intuitively found that the second derivative of those models is highly sparse. Based on this observation...
This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by combining the motion features with the topological features of the cells. The automation of tracking frequently faces problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of varying size and shape, and may have uneven movement;...
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