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Multi-panel medical image segmentation framework for image retrieval system

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Abstract

The automatic segmentation of multi-panel medical images into sub-images improves the retrieval accuracy of medical image retrieval systems. However, the accuracy and efficiency of the available multi-panel medical image segmentation techniques are not satisfactory for multi-panel images containing homogenous color inter-panel borders and image boundary, heterogeneous color inter-panel borders, small size sub-images, or numerous number of sub-images. In order to improve the accuracy and efficiency, a Multi-panel Medical Image Segmentation Framework (MIS-Framework) is proposed and implemented based on locating the longest inter-panel border inside the boundary of the input image. We evaluated the proposed framework on a subset of imageCLEF 2013 dataset containing 2407 images. The proposed framework showed promising experimental results in terms of accuracy and efficiency on single panel as well as multi-panel image class identification and on sub-image separation as compared to the available techniques.

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Acknowledgements

We would like to thank the ImageCLEF organizers for granting access to the imageCLEF 2013 dataset for evaluating the results of our proposed framework. We would also like to thank Dr. Mohsin Ali, and Dr. Iftikhar Ahmad for their contributions in proofreading of the paper.

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Authors and Affiliations

  1. Department of Information Technology, Hazara University, KPK, Pakistan

    Mushtaq Ali

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Le Dong

  3. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, People’s Republic of China

    Rizwan Akhtar

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  1. Mushtaq Ali
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  2. Le Dong
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  3. Rizwan Akhtar
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Correspondence to Mushtaq Ali.

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Ali, M., Dong, L. & Akhtar, R. Multi-panel medical image segmentation framework for image retrieval system. Multimed Tools Appl 77, 20271–20295 (2018). https://doi.org/10.1007/s11042-017-5453-8

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