Video Summarization of Laparoscopic Cholecystectomies

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Links to Files

https://ebiquity.umbc.edu/paper/html/id/453/Video-Summarization-of-Laparoscopic-Cholecystectomies

Permanent Link

http://hdl.handle.net/11603/12023

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-6455-6508
 https://orcid.org/0000-0002-6593-1792
 https://orcid.org/0000-0002-8641-3193

Date

2009-11-14

Type of Work

conference papers and proceedings
preprints
Text

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Program

Citation of Original Publication

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This 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.

Subjects

Video Summarization
Cholecystectomies
Laparoscopic
UMBC Ebiquity Research Group

Abstract

We 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.