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Smoke detection in endoscopic surgery videos: a first step towards retrieval of semantic events
Author(s) -
Loukas Constantinos,
Georgiou Evangelos
Publication year - 2015
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1578
Subject(s) - computer science , smoke , frame (networking) , artificial intelligence , set (abstract data type) , grid , search engine indexing , optical flow , computer vision , image (mathematics) , telecommunications , physics , geometry , mathematics , meteorology , programming language
Background Event‐based annotation of surgical operations has not received much attention, mainly due to diversity of the visual content. As a first attempt at retrieval of surgical events, we address the problem of detecting the smoke produced by electrosurgery tasks. Methods After video decomposition into shots, a grid of particles is placed over the initial frame. The grid is advected with the space–time optical flow and a number of ad hoc kinematic features are extracted. After feature selection, a one‐class support vector machine is employed for classification. A vision‐based fire surveillance method is used for comparison. Results Experimental evaluation is performed on individual shots and laparoscopic cholecystectomy videos. In the first set‐up, average specificity and sensitivity were 86% and 83%, respectively. In video‐based assessment the recognition accuracy was ≥ 80% for two of the three videos tested. The fire surveillance method had a maximum accuracy of 63%. Conclusions The irregular movement of smoke was captured robustly by the proposed features, which could also be employed for interpretation of other semantic occurrences in surgical videos. Copyright © 2014 John Wiley & Sons, Ltd.