A simple and effective sub-image separation method

• Published in: Multimedia tools and applications 2022-05, Vol.81 (11), p.14893-14910
• Main Authors: Ali, Mushtaq, Asghar, Muhammad Zubair, Shah, Mohsin, Mahmood, Toqeer
• Format: Article
• Language: English
• Subjects: Accuracy; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Domains; Hough transformation; Image contrast; Image management; Image retrieval; Medical research; Multimedia Information Systems; Separation; Separators; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11680-8

Image retrieval system has numerous applications in different domains and generated promising results in each domain in case of single panel images. However, in addition to single panel images, the use of multi-panel images particularly in medical and research domains is increasing tremendously. The image retrieval system treats the multi-panel image as a single image and has no potential to get access to its sub-images, as a result the retrieval accuracy of image retrieval system is affected. The latest sub-image separation methods use edge image for locating the position of lines in the input multi-panel image. The edge image of the low contrast multi-panel image often contains broken lines. These broken lines in the edge image cause misdetection of line(s) in the input multi-panel image which in turn affect the accuracy of sub-image separation. Furthermore, their results are not satisfactory for large scale multi-panel images (i.e., multi-panel images having more than seven sub-images along one of its two sides) and multi-panel images having no solid border line(s). In this paper, an effective sub-image separation method for improving the accuracy of sub-image separation is proposed. The proposed method bridges the gaps in each line of the input image using Hough transform. Next, the positions of all lines (i.e., border lines and sub-image separators) in each image are determined and the true border lines among them are identified using a simple border identification model and detached. A fast sub-image separation method is then employed for separating the sub-images of multi-panel image using the true sub-image separators. We tested our proposed method on testing dataset containing 2225 images. The experimental results show that our proposed method outperforms the latest methods.