SuperResolution-aided Recognition of Cytoskeletons in Scanning Probe Microscopy Images

Sara Colantonio, Mario D'Acunto, Marco Righi, Ovidio Salvetti

2014

Abstract

In this paper, we discuss the possibility to adopt SuperResolution (SR) methods as an important preparatory step to Pattern Recognition, so as to improve the accuracy of image content recognition and identification. Actually, SR mainly deals with the task of deriving a high-resolution image from one or multiple low resolution images of the same scene. The high-resolved image corresponds to a more precise image whose content is enriched with information hidden among the pixels of the original low resolution image(s), and corresponds to a more faithfully representation of the imaged scene. Such enriched content obviously represents a better sample of the scene which can be profitably used by Pattern Recognition algorithms. A real application scenario is discussed dealing with the recognition of cell skeletons in Scanning Probe Microscopy (SPM) single image SR. Results show that the SR allows us to detect and recognize important information barely visible in the original low-resolution image.

References

  1. Aliyan, S., Broumandnia, A., 2012. A New Machine Learning Approach to Deblurring License Plate Using K-Means Clustering Method. In International Journal of Advanced Research in Artificial Intelligence, Vol. 1, No. 2, 2012.
  2. Chacko, J.V., Cella Zanacchi, F., Diaspro, A., 2013. Probing Cytoskeletal Structures by Coupling Optical Superresolution and AFM Technqiues for a Correlative Approach, in Cytoskeleton, Wiley, doi:10.1002/cm.21139.
  3. Chang, H., Yeung, D.Y., Xiong, Y., 2004. Superresolution through neighbor embedding. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2004, 275-282.
  4. D'Acunto, M., Salvetti, O., 2011. Pattern recognition methods for thermal drift correction in atomic force microscopy imaging. In Pattern Recognition and Image Analysis - PRIA, vol. 21 (1) pp. 9.
  5. D'Acunto, M., Pieri, G., Righi, M., Salvetti, O., 2013. A Methodological Approach for combining superresolution and pattern recognition to image identification. In Pattern Recognition and Image Analysis - PRIA (in press).
  6. Danti, S., D'Acunto, M., Trombi, L., Berrettini, S., Pietrabissa, A., 2006. A Micro/Nanoscale Surface Mechanical study on Morpho-Functional Changes in Multilineage-Differentiated Human Mesenchimal Stem Cells, in Macromolecular Bioscence.
  7. Fattal, R., 2007. Image upsampling via imposed edge statistics. In Proc. of SIGGRAPH 7807: ACM SIGGRAPH 2007 papers, ACM, 2007.
  8. Freeman, W.T., Jones, T.R., Pasztor, E.C., 2002. Example-based super-resolution. In IEEE Computer Graphics and Applications , 2002, pp. 56-65.
  9. Genovese, C.R., Perone-Pacifico M., Verdinelli I., Wasserman L., 2012. The geometry of nonparametric flament estmation. In Journal of the American Statistical Association, 2012, pp. 788-799.
  10. Irani, M., Peleg, S., 1991 Improving resolution by image registration. In Computer Vision, Graphics and Image Processing 53, 1991, 231-239.
  11. Kim, K. I., Kwon, Y., 2010. Single-image super-resolution using sparse regression and natural image prior. In IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 6, p. 1127-1133.
  12. Lin, Z., Shum, H.Y., 2004 Fundamental limits of reconstruction-based superresolution algorithms under local translation. In IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 2004, 83-97.
  13. Liu, H.Y., Zhang, Y., Song, J.I., 2008. Study on the Methods of Super-resolution Image Reconstruction. In International Society for Photogrammetry and Remote Sensing, vol. XXXVII, pp. 461-466.
  14. Morse, B., Schwartzwald, D., 2001. Image magnification using level set reconstruction. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2001, pp. 333-341.
  15. Park, S.C., Park, M.K., Kang, M.G, 2003. Superresolution image reconstruction: A technical overview. In IEEE Signal Processing Magazine, 2003, 21-36.
  16. Schaap, I.A.T., Carrasco, C., J. de Pablo, P., MacKintosh, F.C., Schmidt, C.F., Elastic Response, Buckling, and Instability of Microtubules under Radial Indentation, Biophysical Journal, Volume 91, Issue 4, 2006, pp. 1521-1531.
  17. Schaap, I., Yang-Ting, C., Szu-Hua, W., Jar-Ferr, Y., 2011. Multi-Resolution Local Probabilistic Approach for Low Resolution Face Recognition, In Proc. of International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI), 2011, p. 220-223, 14-17 Dec. 2011.
  18. Shih-Ming, H., Yang-Ting, C., Szu-Hua, W., Jar-Ferr, Y., 2011. Multi-Resolution Local Probabilistic Approach for Low Resolution Face Recognition, In Proc. of International Conference on Intelligent Computation and Bio-Medical Instrumentation (ICBMI), 2011, p. 220-223, 14-17 Dec. 2011.
  19. Sun, J., Sun, J., Xu, Z., Shum, H.Y., 2008. Image superresolution using gradient profile prior. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008.
  20. Sun, J., Zheng, N.N., Tao, H., Shum, H.Y., 2003. Image hallucination with primal sketch priors. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Volume 2, 2003, 729-736.
  21. Suresh, K.V., Mahesh Kumar, G., Rajagopalan, A.N, 2007. Superresolution of License Plates in Real Traffic Videos. In IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 2, 2007.
  22. Xiong, X., Sun, X., Wu, F., 2009. Image hallucination with feature enhancement. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009.
  23. Yang, J., Wright, J., Huang, T., Ma, Y., 2008. Image super-resolution via sparse representation of raw image patches. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008.
  24. Hawkins, T.L., Spet, D., Mogessie, B., Straube, A., Ross J.L., 2013. Mechanical Properties of Doubly Stabilized Microtubule Filaments. In Biophysical Journal, Cell Press, pp. 1517-1528.
  25. Zuiderveld, K., 1994. Contrast Histogram Equalization. In Academic Press, pp. 474-485.
Download


Paper Citation


in Harvard Style

Colantonio S., D'Acunto M., Righi M. and Salvetti O. (2014). SuperResolution-aided Recognition of Cytoskeletons in Scanning Probe Microscopy Images . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 703-709. DOI: 10.5220/0004830407030709


in Bibtex Style

@conference{icpram14,
author={Sara Colantonio and Mario D'Acunto and Marco Righi and Ovidio Salvetti},
title={SuperResolution-aided Recognition of Cytoskeletons in Scanning Probe Microscopy Images},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={703-709},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004830407030709},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - SuperResolution-aided Recognition of Cytoskeletons in Scanning Probe Microscopy Images
SN - 978-989-758-018-5
AU - Colantonio S.
AU - D'Acunto M.
AU - Righi M.
AU - Salvetti O.
PY - 2014
SP - 703
EP - 709
DO - 10.5220/0004830407030709