学术报告：Enhanced low-rank + sparsity decomposition for speckle reduction in optical coherence tomography

报告时间：星期二（2016.11.15）下午2：00

报告地点：电子信息楼306

报告人： Ivica Kopriva, Ph.D, senior scientist, the Ru?erBoškovi? Institute, Zagreb, Croatia

邀请人：陈新建 特聘教授

Abstract:

Recently, novel data-driven low-rank + sparsity decomposition (LRpD) method was proposed by us to suppress speckle in optical coherence tomography (OCT) image of a retina (I. Kopriva, F. Shi, X. Chen,Journal of Biomedical Optics21 (7), 076008; doi: 0.1117/1.JBO.21.7.076008). In particular, we combine nonconvex regularization-based low-rank approximation of original OCT image with sparsity term that incorporates the speckle. State-of-the-art methods for LRpSD require a priori knowledge of a rank and approximate it with nuclear norm which is not accurate rank indicator. As opposed to that, proposed method provides more accurate approximation of a rank through the use of nonconvex regularization that induces sparse approximation of singular values. Furthermore, a rank value is not required to be known a priori. This, in turn, yields automatic and computationally more efficient method for speckle reduction which yields OCT image with improved contrast-to-noise ratio, contrast and edge fidelity.

Biography:

Ivica Kopriva obtained PhD degree from the Faculty of Electrical Engineering and Computing, University of Zagreb in 1998 with a subject in blind source separation. From 2001 till 2005 he was research and senior research scientist at Department of Electrical and Computer Engineering, The George Washington University, Washington D.C., USA. Since 2006 he is senior scientist at the Ru?erBoškovi? Institute, Zagreb, Croatia. His research interests are related to development of algorithms for unsupervised learning with applications in biomedical image analysis, chemometrics and bioinformatics. He published over 40 papers in internationally recognized journals and holds 3 US patents. He is co-author of the research monograph: Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised and Unsupervised Learning, Springer Series: Studies in Computational Intelligence, 2006. He is senior member of the IEEE and the OSA.