An efficient approach for sub-image separation from large-scale multi-panel images using dynamic programming
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An efficient approach for sub-image separation from large-scale multi-panel images using dynamic programming Mushtaq Ali 1 & Muhammad Zubair Asghar 2 & Amanullah Baloch 1 Received: 16 March 2020 / Revised: 13 August 2020 / Accepted: 17 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Multi-panel images are increasingly used in research and medical domains for describing complicated situations like results’ comparison in paper; or case depiction of a patient by combining all his medical images into a consolidated image. However, the content based image retrieval (CBIR) systems face the issue of performance decline in terms of poor retrieval accuracy because the individual sub-images of the multi-panel images cannot be accessed during the searching process. Representing multi-panel images in the form of sub-images is a necessary step for improving the retrieval accuracy of CBIR systems. The state-of-the-art multi-panel image segmentation approaches use recursive approach for sub-image separation, which detects the location of the sub-lines of a line in the multipanel image appearing in its sub-images repeatedly. This characteristic of the available approaches makes the CBIR incapable to provide the intended results to the end users in real time. In this work, a line detection-based method using dynamic programming is proposed for sub-image separation, which detects the position of every line in the multipanel image only once, instead of several times as in the case of state-of-art approaches. We evaluated the proposed method on a subset of the imageCLEFmed 2013 dataset, containing 1050 images belonging to different groups. The experimental results depict the effectiveness of the proposed method in term of generating the results quickly without losing the accuracy as compare to the state-of-the-art approaches. Keywords Dynamic programming . Multi-panel image . Image segmentation . Image classification . Figure mining . Image retrieval system
* Mushtaq Ali [email protected]
1
Department of IT, Hazara University, Dhodial, Pakistan
2
Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan, Pakistan
Multimedia Tools and Applications
1 Introduction Before the invention of medical images, a painful procedure was used for diagnosing health diseases. For example, for diagnosing the foot fracture, it was needed to move both ends of the foot and if they moved independently then it was decided that the bone has been broken. In order to avoid this painful procedure, the first medical image (X-Ray) was invented in 1895 [7]. Inspired from the satisfactory results of X-Ray imaging technology, Ultrasound, CT-Scan, and MRI imaging technologies were then introduced for diagnosing various health problems. These imaging technologies not only give relief to the patients from the painful procedure, but also assist the physicians in diagnosing the disease. It has been observed that the use of biomedical images in various radiography-related scientifi
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