Advertisement

Least Information Loss (LIL) Conversion of Digital Images and Lessons Learned for Scientific Image Inspection

  • Dalibor Štys
  • Tomáš Náhlík
  • Petr Macháček
  • Renata Rychtáriková
  • Mohammadmehdi Saberioon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9656)

Abstract

Nowadays, most digital images are captured and stored at 16 or 12 bit per pixel integers, however, most personal computers can only display images in 8 bit per pixel integers. Besides, each microarray experiment produces hundreds of images which need larger storage space if images are stored in 16 or 12 bit. This is in most cases done by conversion of single images by an algorithm, which is not apparent to the user. A simple method to avoid the problem is converting 16 or 12-bit images to 8 bit by direct division of the 12-bit intervals into 256 sections and counting the number of points in each of them. Although this approach preserves the proportion of camera signals, it leads to severe loss of information due to losses in intensity depth resolution. The main aim of this article is introducing least information loss (LIL) algorithm as a novel approach to minimize the information loss caused by the transformation the primary camera signals (16 or 12 bit per pixels) to 8 bit per pixel. Least information loss algorithm is based on the omission of unoccupied intensities and transforming remaining points to 8 bit. This approach not only preserve information by storing intervals in the image EXIF file for further analysis, but also it improves object contrast for better visual inspection and object oriented classification. LIL algorithm may be applied also in image series where it enables comparison of primary camera data at scales identical over the whole series. This is particularly important in cases that the coloration is only apparent and reflect various physical processes such as in microscopy imaging.

Keywords

Least information loss algorithm Digital image visual inspection Image series comparison Image conversion 

Notes

Acknowledgments

This work was financially supported by CENAKVA (No. CZ.1.05/2.1.00/01.0024), CENAKVA II (No. LO1205 under the NPU I program) and The CENAKVA Centre Development (No. CZ.1.05/2.1.00/19.0380).

References

  1. 1.
    Macháček, P., Císař, P., Náhlík, T., Rychtáriková, R., Štys, D.: Visual exploration of principles of microscopic image intensities formation using image explorer software. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2016. LNCS, vol. 9656, pp. 537–544. Springer, Heidelberg (2016)Google Scholar
  2. 2.
    Zhyrova, A., Štys, D.: Construction of the phenomenological model of Belousov-Zhabotinsky reaction state trajectory. Int. J. Comput. Math. 91, 4–13 (2014)CrossRefzbMATHGoogle Scholar
  3. 3.
    Rychtáriková, R., Náhlík, T., Smaha, R., Urban, J., Štys Jr., D., Císař, P., Štys, D.: Multifractality in imaging: application of information entropy for observation of inner dynamics inside of an unlabeled living cell in bright-field microscopy. In: Sanayei, A., et al. (eds.) ISCS 2014, vol. 14, pp. 261–267. Springer, Switzerland (2015)Google Scholar
  4. 4.
    Bayer, B.E.: Color imaging array. U. S. Patent No. 3,971,065 (1976)Google Scholar
  5. 5.
  6. 6.
  7. 7.
    Rychtáriková, R., Korbel, J., Macháček, P., Císař, P., Urban, J., Soloviov, D., Štys, D.: Point information gain, point information gain entropy and point information gain entropy density as measures of semantic and syntactic information of multidimensional discrete phenomena, arxiv:1501.02891 (2015)

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Dalibor Štys
    • 1
  • Tomáš Náhlík
    • 1
  • Petr Macháček
    • 1
  • Renata Rychtáriková
    • 1
  • Mohammadmehdi Saberioon
    • 1
  1. 1.Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Institute of Complex SystemsUniversity of South Bohemia in České BudějoviceNové HradyCzech Republic

Personalised recommendations