Semi-Automated Quantitative Validation Tool for Medical Image Processing Algorithm Development

  • Viktor Zoltan JonasEmail author
  • Miklos Kozlovszky
  • Bela Molnar
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 450)


Cancer research and diagnostics is an important frontier to apply the power of computers. Researchers use image processing techniques for a few years now, but diagnostics only start to explore its possibilities. Pathologists specialized in this area usually diagnose by visual inspection, typically through a microscope, or more recently on a computer screen. They examine at tissue specimen or a sample consisting of a population cells extracted from it. The latter area is the area of cytometry that researchers started to support by creating image processing algorithms. The validation of an image processing approach like that is an expensive task both financially and time-wise. This paper aims to show a semi-automatized method to simplify this task, by reducing the amount of human interaction necessary.


Validation tool Automated validation Medical image processing 


  1. 1.
    Jonas, V.Z., Kozlovszky, M., Molnar, B.: Ploidy Analysis on Digital Slides. In: IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI 2013), Hungary, pp. 287–290 (2013). ISBN 978-1-4799-0194-4, doi:10.1109/CINTI.2013.6705207Google Scholar
  2. 2.
    Jonas, V.Z., Kozlovszky, M., Molnar, B.: Nucleus detection on propidium iodide stained digital slides. In: IEEE 9th International Symposium on Applied Computational Intelligence and Informatics (SACI 2014), Timisoara, Romania, pp. 139–143 (2014). doi:10.1109/SACI.2014.6840051Google Scholar
  3. 3.
    Jonas, V.Z., Kozlovszky, M., Molnar, B.: Separation enhanced nucleus detection on propidium iodide stained digital slides. In: IEEE 18th International Conference on Intelligent Engineering Systems (INES 2014), Tihany, Hungary, pp. 157–161 (2014). doi:10.1109/INES.2014.6909360Google Scholar
  4. 4.
    Stathonikos, N., Veta, M., Huisman, A., van Diest, P.J.: Going fully digital: Perspective of a Dutch academic pathology lab. Journal of Pathology Informatics (2013). doi:10.4103/2153-3539.114206Google Scholar
  5. 5.
    Kagadis, G.C., Kloukinas, C., Moore, K., Philbin, J., Papadimitroulas, P., Alexakos, C., Nagy, P.G., Visvikis, D., Hendee, W.R.: Cloud computing in medical imaging. Medical Physics 40 (2013). doi:10.1118/1.4811272Google Scholar
  6. 6.
    Krecsak, L., Micsik, T., Kiszler, G., Krenacs, T., Szabo, D., Jonas, V., Csaszar, G., Czuni, L., Gurzo, P., Ficsor, L., Molnar, B.: Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer. Diagnostic Pathology 6, 6 (2011). doi: 10.1186/1746-1596-6-6 CrossRefGoogle Scholar
  7. 7.
    Zhang, H., Fritts, J.E., Goldman, S.A.: Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding (CVIU) 110(2), 260–280, (2008)Google Scholar
  8. 8.
    Ledig, C., Shi, W., Bai, W., Rueckert, D.: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3065—3072 (2014)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Viktor Zoltan Jonas
    • 1
    Email author
  • Miklos Kozlovszky
    • 2
    • 3
  • Bela Molnar
    • 4
  1. 1.Doctoral School of Applied InformaticsÓbuda UniversityBudapestHungary
  2. 2.Biotech Knowledge CenterÓbuda UniversityBudapestHungary
  3. 3.MTA SZTAKI/Laboratory of Parallel and Distributed ComputingBudapestHungary
  4. 4.Second Department of Internal MedicineSemmelweis UniversityBudapestHungary

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