Grasso, Michael A.Finin, TimZhu, XianshuJoshi, AnupamYesha, Yelena2018-11-162018-11-162009-11-14http://hdl.handle.net/11603/12023Proceedings of the AMIA 2009 Annual Symposium; San Francisco, CA; November 14, 2009 - November 18, 2009We compared image features with a distance metric and support vector machine to identify the critical view of a laparoscopic cholecystectomy. Our accuracy was up to 91%. We are currently experimenting with particle analysis, edge analysis, and feature clus-tering to create a more robust image classifier.1 pageen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Video SummarizationCholecystectomiesLaparoscopicUMBC Ebiquity Research GroupText