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A DCP-based Method for Improving Laparoscopic Images.

Daniel Ruiz-FernándezJuan José Galiana-MerinoAlberto de Ramón-FernándezVíctor Vives-BoixPablo Enríquez-Buendía
Published in: Journal of medical systems (2020)
Laparoscopy is an invasive surgical technique performed in abdominal surgery that provides faster recovery than conventional open surgeries. It requires to introduce a camera to observe the surgical maneuvers. However, during this intervention, the quality of the image may be reduced due to the creation of water vapor and carbon dioxide inside the pelvic-abdominal cavity. This phenomenon produces a nebulous image that causes interruptions during the surgical intervention. Removing this nebulous effect is a key factor to improve the vision of the surgeon. In this study, we have used a method based on the dark channel prior to remove the haze in video frames of laparoscopic surgeries to provide better quality images. The results have been positively evaluated by specialists using real video frames of laparoscopic surgeries, thus demonstrating that this method can be effective in improving the quality of the images without losing any detail of the original image.
Keyphrases
  • deep learning
  • robot assisted
  • convolutional neural network
  • carbon dioxide
  • randomized controlled trial
  • minimally invasive
  • quality improvement
  • machine learning
  • rectal cancer