EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS
Age-related Macular Degeneration (AMD) is a prominent factor contributing to visual impairment in older people, characterized by deterioration of the macula, the center part of the retina. An accurate segmentation of blood vessels is essential for successful intervention and control of MAD. This stu...
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| Format: | Article |
| Language: | English |
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XLESCIENCE
2024-12-01
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| Series: | International Journal of Advances in Signal and Image Sciences |
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| Online Access: | https://xlescience.org/index.php/IJASIS/article/view/178 |
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| author | S Sujatha Pramod Pandey D Gnana Rajesh |
| author_facet | S Sujatha Pramod Pandey D Gnana Rajesh |
| author_sort | S Sujatha |
| collection | DOAJ |
| description | Age-related Macular Degeneration (AMD) is a prominent factor contributing to visual impairment in older people, characterized by deterioration of the macula, the center part of the retina. An accurate segmentation of blood vessels is essential for successful intervention and control of MAD. This study proposes an approach for effectively segmenting blood vessels using U-Net architecture. It is a specialized convolutional neural network that has shown significant potential in accurately segmenting intricate structures captured in medical images. It uses U-Net to precisely define the blood vessels from retinal images, facilitating accurate identification of macula regions for early AMD detection. The efficacy of the proposed method in achieving high accuracy and the computational economy is shown by its evaluation of a large dataset, Structured Analysis of the Retina (STARE). The findings demonstrate that the U-Net-based approach outperforms existing segmentation methods in accuracy and efficiency, making it a promising tool for identifying and monitoring AMD. |
| format | Article |
| id | doaj-art-91326d11dc4a4b97a5c8e882ef252322 |
| institution | Kabale University |
| issn | 2457-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | XLESCIENCE |
| record_format | Article |
| series | International Journal of Advances in Signal and Image Sciences |
| spelling | doaj-art-91326d11dc4a4b97a5c8e882ef2523222025-01-28T06:54:33ZengXLESCIENCEInternational Journal of Advances in Signal and Image Sciences2457-03702024-12-01102485710.29284/ijasis.10.2.2024.48-57206EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSISS SujathaPramod PandeyD Gnana RajeshAge-related Macular Degeneration (AMD) is a prominent factor contributing to visual impairment in older people, characterized by deterioration of the macula, the center part of the retina. An accurate segmentation of blood vessels is essential for successful intervention and control of MAD. This study proposes an approach for effectively segmenting blood vessels using U-Net architecture. It is a specialized convolutional neural network that has shown significant potential in accurately segmenting intricate structures captured in medical images. It uses U-Net to precisely define the blood vessels from retinal images, facilitating accurate identification of macula regions for early AMD detection. The efficacy of the proposed method in achieving high accuracy and the computational economy is shown by its evaluation of a large dataset, Structured Analysis of the Retina (STARE). The findings demonstrate that the U-Net-based approach outperforms existing segmentation methods in accuracy and efficiency, making it a promising tool for identifying and monitoring AMD.https://xlescience.org/index.php/IJASIS/article/view/178biomedical imaging, age-related macular degeneration, deep learning, image analysis, macular deterioration |
| spellingShingle | S Sujatha Pramod Pandey D Gnana Rajesh EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS International Journal of Advances in Signal and Image Sciences biomedical imaging, age-related macular degeneration, deep learning, image analysis, macular deterioration |
| title | EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS |
| title_full | EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS |
| title_fullStr | EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS |
| title_full_unstemmed | EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS |
| title_short | EFFICIENT RETINAL IMAGE SEGMENTATION BY U-NET FOR AGE-RELATED MACULAR DEGENERATION DIAGNOSIS |
| title_sort | efficient retinal image segmentation by u net for age related macular degeneration diagnosis |
| topic | biomedical imaging, age-related macular degeneration, deep learning, image analysis, macular deterioration |
| url | https://xlescience.org/index.php/IJASIS/article/view/178 |
| work_keys_str_mv | AT ssujatha efficientretinalimagesegmentationbyunetforagerelatedmaculardegenerationdiagnosis AT pramodpandey efficientretinalimagesegmentationbyunetforagerelatedmaculardegenerationdiagnosis AT dgnanarajesh efficientretinalimagesegmentationbyunetforagerelatedmaculardegenerationdiagnosis |