Retinal layer segmentation using gradient feature calculation in OCT
Retinal diseases pose significant challenges to global healthcare systems, necessitating accurate and efficient diagnostic methods. Optical Coherence Tomography (OCT) has emerged as a valuable tool for diagnosing and monitoring retinal conditions due to its noncontact and noninvasive nature. This pa...
| Հիմնական հեղինակներ: | , , , , , , |
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| Ձևաչափ: | Հոդված |
| Լեզու: | English |
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World Scientific Publishing
2024-11-01
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| Շարք: | Journal of Innovative Optical Health Sciences |
| Խորագրեր: | |
| Առցանց հասանելիություն: | https://www.worldscientific.com/doi/10.1142/S1793545824500214 |
| _version_ | 1827071112515682304 |
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| author | Lei Liu Yeman Liu Xiaoteng Yan Haiyi Bian Hang Xu Chunzhong Li Hongnan Duan |
| author_facet | Lei Liu Yeman Liu Xiaoteng Yan Haiyi Bian Hang Xu Chunzhong Li Hongnan Duan |
| author_sort | Lei Liu |
| collection | DOAJ |
| description | Retinal diseases pose significant challenges to global healthcare systems, necessitating accurate and efficient diagnostic methods. Optical Coherence Tomography (OCT) has emerged as a valuable tool for diagnosing and monitoring retinal conditions due to its noncontact and noninvasive nature. This paper presents a novel retinal layering method based on OCT images, aimed at enhancing the accuracy of retinal lesion diagnosis. The method utilizes gradient analysis to effectively identify and segment retinal layers. By selecting a column of pixels as a segmentation line and utilizing gradient information from adjacent pixels, the method initiates and proceeds with the layering process. This approach addresses potential issues arising from partial layer overlapping, minimizing deviations in layer segmentation. Experimental results demonstrate the efficacy of the proposed method in accurately segmenting eight retinal boundaries, with an average absolute position deviation of 1.75 pixels. By providing accurate segmentation of retinal layers, this approach contributes to the early detection and management of ocular conditions, ultimately improving patient outcomes and reducing the global burden of vision-related ailments. |
| first_indexed | 2025-03-20T00:33:14Z |
| format | Article |
| id | doaj.art-3f33d27b71a74a5d9d1a5bf2c20cd77c |
| institution | Directory Open Access Journal |
| issn | 1793-5458 1793-7205 |
| language | English |
| last_indexed | 2025-03-20T00:33:14Z |
| publishDate | 2024-11-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Journal of Innovative Optical Health Sciences |
| spelling | doaj.art-3f33d27b71a74a5d9d1a5bf2c20cd77c2024-10-09T11:08:29ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052024-11-01170610.1142/S1793545824500214Retinal layer segmentation using gradient feature calculation in OCTLei Liu0Yeman Liu1Xiaoteng Yan2Haiyi Bian3Hang Xu4Chunzhong Li5Hongnan Duan6Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, P. R. ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, P. R. ChinaDepartment of Ophthalmology, Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, Jiangsu 223003, P. R. ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, P. R. ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, P. R. ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, P. R. ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu 223003, P. R. ChinaRetinal diseases pose significant challenges to global healthcare systems, necessitating accurate and efficient diagnostic methods. Optical Coherence Tomography (OCT) has emerged as a valuable tool for diagnosing and monitoring retinal conditions due to its noncontact and noninvasive nature. This paper presents a novel retinal layering method based on OCT images, aimed at enhancing the accuracy of retinal lesion diagnosis. The method utilizes gradient analysis to effectively identify and segment retinal layers. By selecting a column of pixels as a segmentation line and utilizing gradient information from adjacent pixels, the method initiates and proceeds with the layering process. This approach addresses potential issues arising from partial layer overlapping, minimizing deviations in layer segmentation. Experimental results demonstrate the efficacy of the proposed method in accurately segmenting eight retinal boundaries, with an average absolute position deviation of 1.75 pixels. By providing accurate segmentation of retinal layers, this approach contributes to the early detection and management of ocular conditions, ultimately improving patient outcomes and reducing the global burden of vision-related ailments.https://www.worldscientific.com/doi/10.1142/S1793545824500214Retinal diseasesoptical coherence tomographyretinal layer segmentation |
| spellingShingle | Lei Liu Yeman Liu Xiaoteng Yan Haiyi Bian Hang Xu Chunzhong Li Hongnan Duan Retinal layer segmentation using gradient feature calculation in OCT Journal of Innovative Optical Health Sciences Retinal diseases optical coherence tomography retinal layer segmentation |
| title | Retinal layer segmentation using gradient feature calculation in OCT |
| title_full | Retinal layer segmentation using gradient feature calculation in OCT |
| title_fullStr | Retinal layer segmentation using gradient feature calculation in OCT |
| title_full_unstemmed | Retinal layer segmentation using gradient feature calculation in OCT |
| title_short | Retinal layer segmentation using gradient feature calculation in OCT |
| title_sort | retinal layer segmentation using gradient feature calculation in oct |
| topic | Retinal diseases optical coherence tomography retinal layer segmentation |
| url | https://www.worldscientific.com/doi/10.1142/S1793545824500214 |
| work_keys_str_mv | AT leiliu retinallayersegmentationusinggradientfeaturecalculationinoct AT yemanliu retinallayersegmentationusinggradientfeaturecalculationinoct AT xiaotengyan retinallayersegmentationusinggradientfeaturecalculationinoct AT haiyibian retinallayersegmentationusinggradientfeaturecalculationinoct AT hangxu retinallayersegmentationusinggradientfeaturecalculationinoct AT chunzhongli retinallayersegmentationusinggradientfeaturecalculationinoct AT hongnanduan retinallayersegmentationusinggradientfeaturecalculationinoct |