An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation
In modern healthcare, multi-panel images have become increasingly prevalent in medical diagnosis and treatment, representing about 50% of the medical literature. These images integrate diverse imaging modalities, such as X-rays, CT scans, and MRIs, into a unified composite image, facilitating physic...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10910195/ |
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| author | Faqir Gul Mohsin Shah Mushtaq Ali Tanzeela Qazi Muneer Ahmad Abid Mehmood |
| author_facet | Faqir Gul Mohsin Shah Mushtaq Ali Tanzeela Qazi Muneer Ahmad Abid Mehmood |
| author_sort | Faqir Gul |
| collection | DOAJ |
| description | In modern healthcare, multi-panel images have become increasingly prevalent in medical diagnosis and treatment, representing about 50% of the medical literature. These images integrate diverse imaging modalities, such as X-rays, CT scans, and MRIs, into a unified composite image, facilitating physicians to examine all modalities simultaneously. However, retrieving sub-images from multi-panel images poses a challenge for existing medical image retrieval systems, as they often treat these multi-panel images as single-panel images, thereby restricting access to their constituent sub-images. Consequently, precise segmentation of multi-panel images into sub-images is imperative for effective retrieval. Current segmentation methods are computationally expensive when applied to large-scale, high-resolution multi-panel images. To address this challenge, we propose an adaptive downsampling-based segmentation method that identifies the inter-panel borders separating the sub-images of a multi-panel image by selectively scanning alternate rows and columns, rather than every row and column as done in state-of-the-art approaches. We evaluated the method on a subset of the ImageCLEFmed 2016 dataset, which includes both single-panel and multi-panel images. The experimental results demonstrate that the proposed method significantly reduces computational time while effectively segmenting multi-panel images. |
| format | Article |
| id | doaj-art-8a68721f7dbf4259b609fbb33bba6ee5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8a68721f7dbf4259b609fbb33bba6ee52025-08-20T02:56:03ZengIEEEIEEE Access2169-35362025-01-0113448724488310.1109/ACCESS.2025.354843110910195An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image SegmentationFaqir Gul0https://orcid.org/0009-0002-2955-3680Mohsin Shah1https://orcid.org/0000-0003-2227-9797Mushtaq Ali2https://orcid.org/0000-0002-3697-9498Tanzeela Qazi3Muneer Ahmad4https://orcid.org/0000-0001-5047-1108Abid Mehmood5https://orcid.org/0000-0001-9974-9537Department of Computer Science and Information Technology, Hazara University, Mansehra, PakistanDepartment of Telecommunication, Hazara University, Mansehra, PakistanDepartment of Computer Science and Information Technology, Hazara University, Mansehra, PakistanDepartment of Computer Science and Information Technology, Hazara University, Mansehra, PakistanDepartment of Computer Science, University of Roehampton London, London, U.K.The Beacom College of Computer and Cyber Sciences, Dakota State University, Madison, SD, USAIn modern healthcare, multi-panel images have become increasingly prevalent in medical diagnosis and treatment, representing about 50% of the medical literature. These images integrate diverse imaging modalities, such as X-rays, CT scans, and MRIs, into a unified composite image, facilitating physicians to examine all modalities simultaneously. However, retrieving sub-images from multi-panel images poses a challenge for existing medical image retrieval systems, as they often treat these multi-panel images as single-panel images, thereby restricting access to their constituent sub-images. Consequently, precise segmentation of multi-panel images into sub-images is imperative for effective retrieval. Current segmentation methods are computationally expensive when applied to large-scale, high-resolution multi-panel images. To address this challenge, we propose an adaptive downsampling-based segmentation method that identifies the inter-panel borders separating the sub-images of a multi-panel image by selectively scanning alternate rows and columns, rather than every row and column as done in state-of-the-art approaches. We evaluated the method on a subset of the ImageCLEFmed 2016 dataset, which includes both single-panel and multi-panel images. The experimental results demonstrate that the proposed method significantly reduces computational time while effectively segmenting multi-panel images.https://ieeexplore.ieee.org/document/10910195/Medical image segmentationcontent-based medical image retrievaladaptive downsamplingsub-image segmentationImageCLEFmed |
| spellingShingle | Faqir Gul Mohsin Shah Mushtaq Ali Tanzeela Qazi Muneer Ahmad Abid Mehmood An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation IEEE Access Medical image segmentation content-based medical image retrieval adaptive downsampling sub-image segmentation ImageCLEFmed |
| title | An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation |
| title_full | An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation |
| title_fullStr | An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation |
| title_full_unstemmed | An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation |
| title_short | An Effective Adaptive Downsampling Method for High-Resolution Multi-Panel Image Segmentation |
| title_sort | effective adaptive downsampling method for high resolution multi panel image segmentation |
| topic | Medical image segmentation content-based medical image retrieval adaptive downsampling sub-image segmentation ImageCLEFmed |
| url | https://ieeexplore.ieee.org/document/10910195/ |
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