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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.11.2016 | Original Article

Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework

verfasst von: Constantinos Loukas, Nikolaos Nikiteas, Dimitrios Schizas, Evangelos Georgiou

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2016

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Abstract

Purpose

Over the last decade, the demand for content management of video recordings of surgical procedures has greatly increased. Although a few research methods have been published toward this direction, the related literature is still in its infancy. In this paper, we address the problem of shot detection in endoscopic surgery videos, a fundamental step in content-based video analysis.

Methods

The video is first decomposed into short clips that are processed sequentially. After feature extraction, we employ spatiotemporal Gaussian mixture models (GMM) for each clip and apply a variational Bayesian (VB) algorithm to approximate the posterior distribution of the model parameters. The proper number of components is handled automatically by the VBGMM algorithm. The estimated components are matched along the video sequence via their Kullback–Leibler divergence. Shot borders are defined when component tracking fails, signifying a different visual appearance of the surgical scene.

Results

Experimental evaluation was performed on laparoscopic videos containing a variable number of shots. Performance was measured via precision, recall, coverage and overflow metrics. The proposed method was compared with GMM and a shot detection method based on spatiotemporal motion differences (MotionDiff). The results demonstrate that VBGMM has higher performance than all other methods for most assessment metrics: precision and recall >80 %, coverage: 84 %. Overflow for VBGMM was worse than MotionDiff (37 vs. 27 %).

Conclusions

The proposed method generated promising results for shot border detection. Spatiotemporal modeling via VBGMMs provides a means to explore additional applications such as component tracking.

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Titel: Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework
verfasst von: Constantinos Loukas
Nikolaos Nikiteas
Dimitrios Schizas
Evangelos Georgiou
Publikationsdatum: 01.11.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2016
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-016-1431-2

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Abstract

Purpose

Methods

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Conclusions

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