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Image Stitching Using Normalized Cross-Correlation and the Thresholding Method in a Fluorescence Microscopy Image of Brain Tumor Cells

정규 상호상관도 및 이진화 기법을 이용한 뇌종양 세포의 형광 현미경 영상 스티칭

  • Seo, Ji Hyun (Dept. of Computer science & Engineering, Ewha Womans University) ;
  • Kang, Mi-Sun (Dept. of Computer science & Engineering, Ewha Womans University) ;
  • Kim, Hyun-jung (Seoul School of Integrated Sciences & Technologies (aSSIST)) ;
  • Kim, Myoung-Hee (Dept. of Computer science & Engineering, Ewha Womans University)
  • Received : 2017.01.24
  • Accepted : 2017.05.29
  • Published : 2017.07.31

Abstract

This paper, which covers a fluorescence microscopy image of brain tumor cells, looks at drug reactions by treating different types and concentrations of drugs on a plate of $24{\times}16$ wells. Due to the limitation of the field of view, a well was taken into 9 field images, and each has an overlapping area with its neighboring fields. To analyze more precisely, image stitching is needed. The basic method is finding a similar area using normalized cross-correlation (NCC). The problem is that some overlapping areas may not have any duplicated cells that help to find the matching point. In addition, the cell objects have similar sizes and shapes, which makes distinguishing them difficult. To avoid calculating similarity between blank areas and roughly distinguishing different cells, thresholding is added. The thresholding method classifies background and cell objects based on fixed thresholds and finds the location of the first seen cell. After getting its location, NCC is used to find the best correlation point. The results are compared with a simple boundary stitched image. Our proposed method stitches images that are connected in a grid form without collision, selecting the best correlation point among areas that contain overlapping cells and ones without it.

Keywords

References

  1. A.A. Goshtasby, 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, Wiley Press, Hoboken, New Jersey, 2005.
  2. C. Sun, R. Beare, V. Hilsenstein, and P. Jackway, "Mosaicing of Microscope Images with Global Geometric and Radiometric Corrections," Journal of Microscopy, Vol. 224, pp. 158-165, 2006. https://doi.org/10.1111/j.1365-2818.2006.01687.x
  3. B. Ma, T. Zimmermann, M. Rohde, S. Winkelbach, F. He, W. Lindenmaier, et al., "Use of Autostitch for Automatic Stitching of Microscope Images," Micron, Vol. 38, Issue 5, pp. 492-499, 2007. https://doi.org/10.1016/j.micron.2006.07.027
  4. D.J. Kang and J.E. Ha, Digital Image Processing Using Visual C ++, SciTech Media, Gyeonggi-do, Korea, 2003.
  5. S. Park, S. Park, J. Lee, J. Shin, and Y. Shin, “High-Quality Stitching Method of 3D Multiple Dental CT Images,” Journal of Korea Multimedia Society, Vol. 17, No. 10, pp. 1205-1212, 2014. https://doi.org/10.9717/kmms.2014.17.10.1205
  6. H. Cho, H. Kye, and J. Lee, “Rapid Stitching Method of Digital X-ray Images Using Template-based Registration,” Journal of Korea Multimedia Society, Vol. 18, No. 6, pp. 701-709, 2015. https://doi.org/10.9717/kmms.2015.18.6.701
  7. V. Rankov, R.J. Locke, R.J. Edens, P.R. Barber, and B. Vojnovic, "An Algorithm for Image Stitching and Blending," Proceedings of SPIE, Vol. 5701, pp. 190-199, 2005.
  8. P.M. Jain and V.K. Shandliya, "A Review Paper on Various Approaches for Image Mosaicing," International Journal of Computational Engineering Research, Vol. 3, Issue 4, pp. 106-109, 2013.
  9. H. Kim, J.U. Lee, and H. Hong, “Automatic Stitching of Pathological Prostate Images Using Geometric Correction and Rigid Registration,” Journal of KISS : Software and Applications, Vol. 39, No. 12, pp. 955-964, 2012.