Desmoking Laparoscopy Surgery Images Using an Image-to-Image Translation Guided by an Embedded Dark Channel

Autor: Sebastián Salazar-Colores, César J. Ortiz-Echeverri, Gerardo Flores, Hugo Moreno Jimenez
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
General Computer Science
Channel (digital image)
Computer science
Computer Vision and Pattern Recognition (cs.CV)
image smoke removal
Computer Science - Computer Vision and Pattern Recognition
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Image (mathematics)
conditional generative adversarial network
FOS: Electrical engineering
electronic engineering
information engineering
General Materials Science
Computer vision
Artificial neural network
business.industry
Image and Video Processing (eess.IV)
General Engineering
Electrical Engineering and Systems Science - Image and Video Processing
Frame rate
Image translation
Laparoscopy
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
dark channel
business
lcsh:TK1-9971
Zdroj: IEEE Access, Vol 8, Pp 208898-208909 (2020)
ISSN: 2169-3536
DOI: 10.1109/access.2020.3038437
Popis: In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then producing negative consequences on the patient's health. In this paper, a novel computational approach to remove the smoke effects is introduced. The proposed method is based on an image-to-image conditional generative adversarial network in which a dark channel is used as an embedded guide mask. Obtained experimental results are evaluated and compared quantitatively with other desmoking and dehazing state-of-art methods using the metrics of the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. Based on these metrics, it is found that the proposed method has improved performance compared to the state-of-the-art. Moreover, the processing time required by our method is 92 frames per second, and thus, it can be applied in a real-time medical system trough an embedded device.
Databáze: OpenAIRE
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