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 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.