Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5269–5285 | Cite as

Retrieve similar cell images in OpenSlide file

  • Jae Gu Lee
  • Young Woong KoEmail author


Computer-based image analysis system enables efficient retrieval of similar images from large-size pathology database. In such a system, images are expressed based on visual content characteristics, and similarities between images are obtained by comparing the features. A pathology image is usually very huge and expressed as several layer of image quality called OpenSlide. To find similar cells from a OpenSlide file, we have to use high performance computer equiped with multi-core and large size memory. In this paper, we propose a method to find similar cell images with resource limited computer. For this purpose, we exploit several technique to minimize system resource requirement and adapt imaging process scheme that enhances the accuracy of finding similar cell images from a OpenSlide file. We adapt a leveling, tiling and sub tiling to the OpenSlide file and extracting the feature points accurately using the hybrid feature extracting algorithm that adapts advantages of ORB and Blob algorithm. Furthermore, grayscale and histogram schemes are used to improve the accuracy of finding similar pathology cell images. Experiment results show that the proposed system improves the performance of the system and increases the accuracy of finding similar images efficiently.


OpenSlide Leveling Tiling Image sync Feature extraction Image similarity 



This research was supported by Hallym University Research Fund, 2016(HRF-201608-009) and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and future Planning (2016H1D5A1910630).


  1. 1.
    Abdel-Maksoud E, Elmogy M, Al-Awadi R (2015) Brain tumor segmentation based on a hybrid clustering technique. Egypt Inf J 16(1):71–81CrossRefGoogle Scholar
  2. 2.
    Elmore JG et al (2015) Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313(11):1122–1132CrossRefGoogle Scholar
  3. 3.
    Goode A et al (2013) OpenSlide: A vendor-neutral software foundation for digital pathology. J Pathol Inf 4(1):27CrossRefGoogle Scholar
  4. 4.
    Guo JM, Prasetyo H (2015) Content-Based Image Retrieval Using Features Extracted From Halftoning-Based Block Truncation Coding. IEEE Trans Image Process 24(3):1010–1024MathSciNetCrossRefGoogle Scholar
  5. 5.
    Jyothi B, MadhaveeLatha Y, Mohan PGK (2015) An effective multiple visual features for Content Based Medical Image Retrieval. IEEE 9th International Conference on Intelligent Systems and Control, pp 1–5Google Scholar
  6. 6.
    Kumar A, Nette F, Klein K, Fulham M, Kim J (2015) A Visual Analytics Approach Using the Exploration of Multidimensional Feature Spaces for Content-Based Medical Image Retrieval. IEEE J Biomed Health Inf 19(5):1734–1746CrossRefGoogle Scholar
  7. 7.
    Leitloff J, Hinz S, Stilla U (2010) Vehicle detection in very high resolution satellite images of city areas. IEEE Trans Geosci Remote Sens 48(7):2795–2806CrossRefGoogle Scholar
  8. 8.
    Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. Pattern Recognition (ICPR), 2012 21st International Conference on. IEEEGoogle Scholar
  9. 9.
    Pan H et al (2014) Brain CT image similarity retrieval method based on uncertain location graph. IEEE J Biomed Health Inf 18(2):574–584CrossRefGoogle Scholar
  10. 10.
    Pan X, Lyu S (2010) Region duplication detection using image feature matching. IEEE Trans Inf Forensics Secur 5(4):857–867CrossRefGoogle Scholar
  11. 11.
    Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. Computer Vision–ECCV 2006, pp 430–443Google Scholar
  12. 12.
    Roy S, Chi Y, Liu J, Venkatesh SK, Brown MS (2014) Three-Dimensional Spatiotemporal Features for Fast Content-Based Retrieval of Focal Liver Lesions. IEEE Trans Biomed Eng 61(11):2768–2778CrossRefGoogle Scholar
  13. 13.
    Rublee E et al (2011) ORB: An efficient alternative to SIFT or SURF. Computer Vision (ICCV), 2011 I.E. international conference on. IEEEGoogle Scholar
  14. 14.
    Xu H, Lu C, Berendt R, Jha N, Mandal M (2017) Automatic Nuclear Segmentation Using Multi-scale Radial Line Scanning with Dynamic Programming. IEEE Trans Biomed Eng 64(10):2475–2485CrossRefGoogle Scholar
  15. 15.
    Zhang X, Liu W, Dundar M, Badve S, Zhang S (2015) Towards Large-Scale Histopathological Image Analysis: Hashing-Based Image Retrieval. IEEE Trans Med Imaging 34(2):496–506CrossRefGoogle Scholar
  16. 16.
    Zheng Y, Jiang Z, Shi J, Ma Y (2014) Retrieval of pathology image for breast cancer using PLSA model based on texture and pathological features. In Image Processing (ICIP), 2014 I.E. International Conference. IEEE, pp 2304–2308Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Computer EngineeringHallym UniversityChuncheonRepublic of Korea

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