Specular Highlight Detection Based on Color Distribution for Endoscopic Images
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-01-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2020.616930/full |
| _version_ | 1811271054664925184 |
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| author | Baoxian Yu Baoxian Yu Baoxian Yu Wanbing Chen Wanbing Chen Qinghua Zhong Qinghua Zhong Han Zhang Han Zhang Han Zhang |
| author_facet | Baoxian Yu Baoxian Yu Baoxian Yu Wanbing Chen Wanbing Chen Qinghua Zhong Qinghua Zhong Han Zhang Han Zhang Han Zhang |
| author_sort | Baoxian Yu |
| collection | DOAJ |
| description | 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 nonhighlight area of endoscopic images are higher than those 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 with respect to 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. |
| first_indexed | 2024-04-12T22:13:26Z |
| format | Article |
| id | doaj.art-be9ac40d8d9b43a3a79dc9e82b808498 |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-12T22:13:26Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-be9ac40d8d9b43a3a79dc9e82b8084982022-12-22T03:14:39ZengFrontiers Media S.A.Frontiers in Physics2296-424X2021-01-01810.3389/fphy.2020.616930616930Specular Highlight Detection Based on Color Distribution for Endoscopic ImagesBaoxian Yu0Baoxian Yu1Baoxian Yu2Wanbing Chen3Wanbing Chen4Qinghua Zhong5Qinghua Zhong6Han Zhang7Han Zhang8Han Zhang9School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaGuangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, South China Normal University, Guangzhou, ChinaSCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaGuangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, South China Normal University, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaGuangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, South China Normal University, Guangzhou, ChinaSchool of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, ChinaGuangdong Provincial Engineering Technology Research Center of Cardiovascular Individual Medicine and Big Data, South China Normal University, Guangzhou, ChinaSCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan, ChinaEndoscopic 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 nonhighlight area of endoscopic images are higher than those 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 with respect to 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.https://www.frontiersin.org/articles/10.3389/fphy.2020.616930/fulladaptive detectioncolor distributionendoscopic imagesRGB color spacespecular highlight detection |
| spellingShingle | Baoxian Yu Baoxian Yu Baoxian Yu Wanbing Chen Wanbing Chen Qinghua Zhong Qinghua Zhong Han Zhang Han Zhang Han Zhang Specular Highlight Detection Based on Color Distribution for Endoscopic Images Frontiers in Physics adaptive detection color distribution endoscopic images RGB color space specular highlight detection |
| title | Specular Highlight Detection Based on Color Distribution for Endoscopic Images |
| title_full | Specular Highlight Detection Based on Color Distribution for Endoscopic Images |
| title_fullStr | Specular Highlight Detection Based on Color Distribution for Endoscopic Images |
| title_full_unstemmed | Specular Highlight Detection Based on Color Distribution for Endoscopic Images |
| title_short | Specular Highlight Detection Based on Color Distribution for Endoscopic Images |
| title_sort | specular highlight detection based on color distribution for endoscopic images |
| topic | adaptive detection color distribution endoscopic images RGB color space specular highlight detection |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2020.616930/full |
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