Name
Abstract | ||
|---|---|---|
Laparoscopic surgery has a limited field of view. Laser ablation in a laproscopic surgery causes smoke, which inevitably influences the surgeonu0027s 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.2352/issn.2169-2629.2018.26.163 | color imaging conference |
DocType | Volume | Issue |
Journal | abs/1812.10784 | 1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congcong Wang | 1 | 0 | 0.34 |
| Vivek Sharma | 2 | 15 | 4.92 |
| Yu Fan | 3 | 0 | 0.34 |
| Faouzi Alaya Cheikh | 4 | 168 | 38.47 |
| Azeddine Beghdadi | 5 | 562 | 83.96 |
| Ole Jacob Elle | 6 | 0 | 0.34 |
| Rainer Stiefelhagen | 7 | 3512 | 274.86 |