PRIAR using a Graph Segmentation Method

M. Righi, M. D’Acunto, O. Salvetti

2015

Abstract

Recently, we have suggested a simple and general-purpose method able to combine high-resolution analysis with the classification and identification of components of microscopy imaging. The method named PRIAR (Pattern Recognition Image Augumented Resolution) is a tool developed by the authors that gives the possibility to enhance spatial and photometric resolution of low-res images. The implemented algorithm follows the scheme: 1) image classification; 2) blind super-resolution on single frame; 3) pattern-analysis; 4) reconstruction of the discovered pattern. In this paper, we suggest some improvements of the PRIAR algorithm, in particular, the definition of a segmentation method which is based on homomorphism between a processed image and a graph describing the image itself, able to identify object of interest in complex patterns. The case study is the identification of organs inside biological cells acquired with Atomic Force Microscopy Technique.

References

  1. Ardizzone, E. et al. (2009). Fuzzy-based kernel regression approaches for free form deformation and elastic registration of medical images. In de Mello, C. A. B., editor, Biomedical Engineering. nTech.
  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer-Verlag New York, Inc.
  3. Bourke, P. (2001). Bicubic Interpolation for Image Scaling.
  4. DAcunto, M. (2011). Nanotribology and Biomaterials: New Challenges for Atomic Force Microsopy. In Advances in Nanotechnology, Advances in Nanotechnology, pages 105142. Nova Science.
  5. DAcunto, M. et al. (2015). A new method combining Enhanced Resolution and Pattern Identification. submitted to The Open Medical Informatics Journal.
  6. Getreuer, P. (2011). Linear Methods for Image Interpolation. Image Processing On Line, 18.
  7. Gonzalez, R. C. et al. (2010). Digital Image Processing Using MATLAB. Prentice Hall, 2nd edition.
  8. Gonzalez, R. C. and Woods, R. E. (2008). Digital image processing. Prentice Hall, Upper Saddle River, N.J.
  9. Hopcroft, J. E. and Ullman, J. D. (1969). Formal Languages and Their Relation to Automata. AddisonWesley Longman Publishing Co., Inc., Boston, MA, USA.
  10. Ikonen, L. and Toivanen, P. J. (2005). Distance and Nearest Neighbor Transforms of Gray-Level Surfaces Using Priority Pixel Queue Algorithm. In Blanc-Talon, J. et al., editors, ACIVS, volume 3708 of Lecture Notes in Computer Science, pages 308315. Springer.
  11. Kim, K. I. and Kwon, Y. (2010). Single-Image SuperResolution Using Sparse Regression and Natural Image Prior. IEEE Trans. Pattern Anal. Mach. Intell., 32(6):11271133.
  12. Parinya, C. et al. (2014). Pre-Reduction Graph Products: Hardnesses of Properly Learning DFAs and Approximating EDP on DAGs. In Foundations of Computer Science (FOCS), 2014 IEEE 55th Annual Symposium on, pages 444453. IEEE.
  13. Peng, B. et al. (2013). A Survey of Graph Theoretical Approaches to Image Segmentation. Pattern Recogn., 46(3):10201038.
  14. Righi, M. (2014). Pattern Recognition Image Augmented Resolution: a tool for image analysis. Technical Report.
  15. Righi, M. et al. (2014). PRIAR (Pattern Recognition Image Augmented Resolution) a tool to combine patternrecognition with superresolution. In 9th International Conference on Open German Russian Workshop on Pattern Recognition and Image Understanding.
  16. Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Prentice Hall, 3 edition.
  17. Sonka, M. et al. (2007). Image Processing, Analysis, and Machine Vision. Thomson-Engineering.
  18. Theodoridis, S. et al. (2008). Pattern Recognition. Academic Press, 4th edition.
  19. Wertheimer, M. (1938). Laws of organization in perceptual forms. In Ellis, W., editor, A Source Book of Gestalt Psychology, pages 7188. Routledge and Kegan Paul, London.
  20. Zhang, J. (2008). Complexity and Universality of Iterated Finite Automata. Complex Systems, 18.
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Paper Citation


in Harvard Style

Righi M., D’Acunto M. and Salvetti O. (2015). PRIAR using a Graph Segmentation Method . In Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015) ISBN 978-989-758-094-9, pages 46-52. DOI: 10.5220/0005461600460052


in Bibtex Style

@conference{imta-515,
author={M. Righi and M. D’Acunto and O. Salvetti},
title={PRIAR using a Graph Segmentation Method},
booktitle={Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015)},
year={2015},
pages={46-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005461600460052},
isbn={978-989-758-094-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Workshop on Image Mining. Theory and Applications - Volume 1: IMTA-5, (VISIGRAPP 2015)
TI - PRIAR using a Graph Segmentation Method
SN - 978-989-758-094-9
AU - Righi M.
AU - D’Acunto M.
AU - Salvetti O.
PY - 2015
SP - 46
EP - 52
DO - 10.5220/0005461600460052