Endoscopic imaging systems have been widely used in disease diagnosis and minimally invasive surgery. Practically, specular reflection (a.k.a. highlight) always exists in endoscopic images, and significantly affects surgeons' observation and judgment. Motivated by the fact that the values of the red channel in non-highlight area of endoscopic images are higher than that of the green and blue ones, this paper proposes an adaptive specular highlight detection method for endoscopic images. Specifically, for each pixel, we design a criterion for specular highlight detection based on the ratio of the red channel to both the green and blue channels. With the designed criteria, we take advantage of image segmentation, and then develop an adaptive threshold w.r.t. the differences between the red channel and the other ones of neighboring pixels. To validate the proposed method, we conduct experiments on clinical data and CVC-ClinicSpec open database. The experimental results demonstrate that the proposed method yields an averaged Precision, Accuracy and F1-score rate of 88.76%, 99.60% and 72.56%, respectively, and outperforms the state-of-the-art approaches based on color distribution reported for endoscopic highlight detection.

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The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative Agreement NNX16AC86A