Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence
Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal...
| Main Authors: | , , , , , , |
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| Format: | Article |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1118087/full |
| _version_ | 1811164219319517184 |
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| author | Peng Yuan Ruxue Bai Yan Yan Shijie Li Jing Wang Changqi Cao Qi Wu |
| author_facet | Peng Yuan Ruxue Bai Yan Yan Shijie Li Jing Wang Changqi Cao Qi Wu |
| author_sort | Peng Yuan |
| collection | DOAJ |
| description | Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers. |
| first_indexed | 2024-04-10T15:17:55Z |
| format | Article |
| id | doaj.art-0b178c4dbc6549ad992c78ecd0fdd35b |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-10T15:17:55Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-0b178c4dbc6549ad992c78ecd0fdd35b2023-02-14T17:36:35ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-02-011610.3389/fnins.2022.11180871118087Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligencePeng Yuan0Ruxue Bai1Yan Yan2Shijie Li3Jing Wang4Changqi Cao5Qi Wu6The Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, ChinaFaculty of Information Technology, Beijing University of Technology, Beijing, ChinaThe Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, ChinaThe Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, ChinaThe Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, ChinaThe Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, ChinaThe Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Endoscopy, Peking University Cancer Hospital and Institute, Beijing, ChinaGastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.https://www.frontiersin.org/articles/10.3389/fnins.2022.1118087/fullgastroscope imagesmotion blursubjective and objective quality assessmenthuman visual systemsemi-full combination subspace |
| spellingShingle | Peng Yuan Ruxue Bai Yan Yan Shijie Li Jing Wang Changqi Cao Qi Wu Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence Frontiers in Neuroscience gastroscope images motion blur subjective and objective quality assessment human visual system semi-full combination subspace |
| title | Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence |
| title_full | Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence |
| title_fullStr | Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence |
| title_full_unstemmed | Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence |
| title_short | Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence |
| title_sort | subjective and objective quality assessment of gastrointestinal endoscopy images from manual operation to artificial intelligence |
| topic | gastroscope images motion blur subjective and objective quality assessment human visual system semi-full combination subspace |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1118087/full |
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