报告时间：星期三（2016.4.20）10：00am
报告地点：电子信息楼306
报告人：Ivica Kopriva，Senior Scientist, Ru?er Boškovi? Institute, Crotia
邀请人：陈新建特聘教授
报告摘要：Recently, novel data-driven offset-sparsity decomposition (OSD) method was proposed by us to increase colorimetric difference between tissue-structures present in the color microscopic image of stained specimen in histopathology (Kopriva et al, Journal of Biomedical Optics 20 (7), 076012 (July 28, 2015); doi: 10.1117/1.JBO.20.7.076012). The method decomposes vectorized spectral images into offset terms and sparse terms. A sparse term represents an enhanced image, and an offset term represents a “shadow.” The related optimization problem is solved by computational improvement of the accelerated proximal gradient method used initially to solve the related rank-sparsity decomposition problem. Removal of an image-adapted color offset yields an enhanced image with improved colorimetric differences among the histological structures. This is verified by a no-reference colorfulness measure estimated from 37 specimens of the human liver and 1 specimen of the mouse liver stained with various stains. The colorimetric difference improves on average by 43.86% with a 99% confidence interval (CI) of [35.35%, 51.62%]. Furthermore, according to the mean opinion score, estimated on the basis of the evaluations of five pathologists, images enhanced by the proposed method exhibit an average quality improvement of 16.60% with a 99% CI of [10.46%, 22.73%].
报告人简介：
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?er Boškovi? 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.