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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10910195/ |
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| Summary: | 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. |
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| ISSN: | 2169-3536 |