Paper Info
Title
Relevance Segmentation of Laparoscopic Videos
Abstract
In recent years, it became common to record video footage of laparoscopic surgeries. This leads to large video archives that are very hard to manage. They often contain a considerable portion of completely irrelevant scenes which waste storage capacity and hamper an efficient retrieval of relevant scenes. In this paper we (1) define three classes of irrelevant segments, (2) propose visual feature extraction methods to obtain irrelevance indicators for each class and (3) present an extensible framework to detect irrelevant segments in laparoscopic videos. The framework includes a training component that learns a prediction model using nonlinear regression with a generalized logistic function and a segment composition algorithm that derives segment boundaries from the fuzzy frame classifications. The experimental results show that our method performs very good both for the classification of individual frames and the detection of segment boundaries in videos and enables considerable storage space savings.
Year
DOI
Venue
2013
10.1109/ISM.2013.22
ISM
Keywords
Field
DocType
segment composition algorithm,extensible framework,considerable portion,laparoscopic surgery,considerable storage space saving,irrelevant segment,relevance segmentation,large video archives,laparoscopic video,segment boundary,laparoscopic videos,irrelevant scene,feature extraction,image classification,regression analysis,image segmentation,surgery,fuzzy classification,fuzzy set theory,information retrieval systems
Computer vision,Object detection,Pattern recognition,Fuzzy classification,Computer science,Segmentation,Generalised logistic function,Image segmentation,Feature extraction,Fuzzy set,Artificial intelligence,Contextual image classification
Conference
Citations 
PageRank 
References 
10
0.62
0
Authors
3
Name
Order
Citations
PageRank
Bernd Münzer19814.94
Klaus Schoeffmann250963.01
Laszlo Böszörmenyi343325.29