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

TL;DR

This paper introduces an image enhancement technique based on weighted least squares optimization to improve smoke detection in laparoscopic surgery images, leading to better classification accuracy.

Contribution

It proposes a novel enhancement method that improves smoke/non-smoke image classification accuracy in laparoscopic surgery using statistical features and SVM.

Findings

Achieved 4% improvement in accuracy and F1-Score over baseline RGB images.

Outperformed existing saturation-based classification methods by 1/5% and 1/6%.

Demonstrated effectiveness on the Cholec80 dataset.

Abstract

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.