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Enhanced image similarity analysis system in digital pathology

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Abstract

In digital pathology, image similarity algorithms are used to find cancer in tissue cells from medical images. However, it is very difficult to apply image similarity algorithms used in general purpose system. Because in the medical field, accuracy and reliability must be perfect when looking for cancer cells by using image similarity techniques to pathology images. To cope with this problem, this paper proposes an efficient similar image search algorithm for digital pathology by applying leveling and tiling scheme on OpenSlide format. Furthermore, we apply image sync method to extract feature key points during image similarity processing. In the experiment, to prove the efficiency of the proposed system, we conduct several experiments including algorithm performance, algorithm accuracy and computation time. The experiments result shows that the proposed system efficiently retrieves similar cell images from pathology images.

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Acknowledgements

This research was supported by Hallym University Research Fund 2015(H20150684) and this research was also supported by The Leading Human Resource Training Program of Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (2016H1D5A1910630).

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Authors and Affiliations

  1. Department of Computer Engineering, Hallym University, Chuncheon, Gangwon, 200-702, Republic of Korea

    Jae-Gu Lee, Seung-Ho Yeon & Young-Woong Ko

  2. Department of Pathology, Chuncheon Sacred Heart Hospital, Hallym University, Chuncheon, Gangwon, 200-702, Republic of Korea

    Kyung-Chan Choi

  3. Department of Pathology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, 07441, Republic of Korea

    Jeong Won Kim

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  1. Jae-Gu Lee
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  2. Kyung-Chan Choi
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  3. Seung-Ho Yeon
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  4. Jeong Won Kim
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  5. Young-Woong Ko
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Correspondence to Young-Woong Ko.

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Lee, JG., Choi, KC., Yeon, SH. et al. Enhanced image similarity analysis system in digital pathology. Multimed Tools Appl 76, 25477–25494 (2017). https://doi.org/10.1007/s11042-017-4773-z

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  • DOI: https://doi.org/10.1007/s11042-017-4773-z

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