Real-Time Image-based Smoke Detection in Endoscopic Videos

Leibetseder, Andreas; Primus, Manfred Jürgen; Petscharnig, Stefan; Schoeffmann, Klaus

doi:10.1145/3126686.3126690

Cited by 23 publications

(22 citation statements)

References 32 publications

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“…In this section, we demonstrate the use of our proposed enhancement method on a challenging smoke/non-smoke classification dataset [30,36]. We first introduce the dataset, followed by a thorough analysis of the proposed method.…”

Section: Methodsmentioning

confidence: 99%

“…Dataset. We conduct experiments on Cholec80 dataset [36] which contains 80 videos of cholecystectomy surgeries manually labeled with smoke/non-smoke image sequence by [30] 2 . The dataset in overall contains approximately 100K annotated images, in particular between 200-1300 images of smoke/non-smoke in each video.…”

Section: Methodsmentioning

confidence: 99%

“…The performance of their CNN models on Cholec80 [36] dataset are very similar to that of SAN or SPA. While DL shows its promising performance on smoke classification [9,30], to the best of our knowledge there is no work that does image enhancement for smoke classification. Therefore, we limit to comparisons with the non deep learning based methods only in our experimental section.…”

Section: Introductionmentioning

confidence: 99%

“…In the medical imaging community, researchers have shown that the formation of smoke and the lighting condition for laparoscopic surgery images are very different, which limits the "in the wild" smoke detection methodologies applicability in medical domain [9,30]. Recently, Chou et al [31] explore temporal differences information that is motion blur and block analysis between current and previous frames as features for smoke detection.…”

Section: Introductionmentioning

confidence: 99%

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Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?

Wang

Sharma

Fan

et al. 2018

cic

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Laparoscopic surgery has a limited field of view. Laser ablation in a laproscopic surgery causes smoke, which inevitably influences the surgeon's visibility. Therefore, it is of vital importance to remove the smoke, such that a clear visualization is possible. In order to employ a desmoking technique, one needs to know beforehand if the image contains smoke or not, to this date, there exists no accurate method that could classify the smoke/non-smoke images completely. In this work, we propose a new enhancement method which enhances the informative details in the RGB images for discrimination of smoke/non-smoke images. Our proposed method utilizes weighted least squares optimization framework (WLS). For feature extraction, we use statistical features based on bivariate histogram distribution of gradient magnitude (GM) and Laplacian of Gaussian (LoG). We then train a SVM classifier with binary smoke/non-smoke classification task. We demonstrate the effectiveness of our method on Cholec80 dataset. Experiments using our proposed enhancement method show promising results with improvements of 4% in accuracy and 4% in F1-Score over the baseline performance of RGB images. In addition, our approach improves over the saturation histogram based classification methodologies Saturation Analysis (SAN) and Saturation Peak Analysis (SPA) by 1/5% and 1/6% in accuracy/F1-Score metrics.We can employ our enhancement method in replacement of RGB images for classifier training e.g., CNN architectures, which in turn can lead to more accurate classification. Code will be released for public use.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?

Wang

Sharma

Fan

et al. 2018

cic

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“…The threshold used is t c = 0.35 as suggested in the original work. 16 The probability of an image having smoke p (S) and no smoke p (N S) are therefore defined as…”

Section: Smokementioning

confidence: 99%

Towards a video quality assessment based framework for enhancement of laparoscopic videos

Khan

Beghdadi

Cheikh³

et al. 2020

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment

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Laparoscopic videos can be affected by different distortions which may impact the performance of surgery and introduce surgical errors. In this work, we propose a framework for automatically detecting and identifying such distortions and their severity using video quality assessment. There are three major contributions presented in this work (i) a proposal for a novel video enhancement framework for laparoscopic surgery; (ii) a publicly available database for quality assessment of laparoscopic videos evaluated by expert as well as non-expert observers and (iii) objective video quality assessment of laparoscopic videos including their correlations with expert and non-expert scores.

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