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Performance comparison of various feature detector-descriptors and temporal models for video-based assessment of
Constantinos Loukas1, Evangelos Georgiou1
1Medical Physics Lab-Simulation Center, School of Medicine, University of Athens, Greece.
Background:
Despite the significant progress in hand gesture analysis for surgical skills assessment, video-based analysis has not received much attention. In this study we investigate the application of various feature detector-descriptors and temporal modeling techniques for laparoscopic skills assessment.
Methods:
Two different setups were designed: static and dynamic video-histogram analysis. Four well-known feature detection-extraction methods were investigated: SIFT, SURF, STAR-BRIEF and STIP-HOG. For the dynamic setup two temporal models were employed (LDS and GMMAR model). Each method was evaluated for its ability to classify experts and novices on peg transfer and knot tying.
Results:
STIP-HOG yielded the best performance (static: 74-79%; dynamic: 80-89%). Temporal models had equivalent performance. Important differences were found between the two groups with respect to the underlying dynamics of the video-histogram sequences.
Conclusions:
Temporal modeling of feature histograms extracted from laparoscopic training videos provides information about the skill level and motion pattern of the operator. Copyright © 2015 John Wiley & Sons, Ltd.
