Advertisement

Background Extraction in Electron Microscope Images of Artificial Membranes

  • A. Karathanou
  • J.-L. Buessler
  • H. Kihl
  • J.-P. Urban
Conference paper
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

On-line analysis of Transmission Electron Microscope (TEM) images is a field with great interest that opens up new prospects regarding automatic acquisitions. Presently, our work is focused on the automatic identification of artificial membranes derived from 2D protein crystallization experiments. Objects recognition at medium magnification aims to control the microscope in order to acquire interesting membranes at high magnification. A multiresolution segmentation technique has been proposed for the image partition. This paper presents an analysis of this partition to extract the background. To achieve this goal in very noisy images, it is essential to suppress false contours as they split the background into multiple regions. Statistical properties of such regions are not always sufficient for their identification as background. The analysis of these regions contours was therefore considered. In the proposed solution, the elimination of false contours is based on the statistical examination of the perpendicular gradient component along the contour. After this improved segmentation, the background extraction can be easily effectuated since this resulting region appears bright and large.

Keywords

Transmission Electron Microscope Image Electron Microscope Image Background Region Membrane Region Artificial Membrane 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    HT3DEM: High Throughput — Three Dimensional Electron Microscopy, http://www.ht3dem.org/
  2. 2.
    N. Coudray, J.-L. Buessler, H. Kihl, J.-P. Urban “TEM Images of membranes: A mul-tiresolution edge-detection approach for watershed segmentation”, in Physics in Signal and Image Processing (PSIP), 2007Google Scholar
  3. 3.
    L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, pp. 583–598, 1991Google Scholar
  4. 4.
    Karathanou, J.-L. Buessler, H. Kihl, and J.-P. Urban, “Detection of low contrasted membranes in electron microscope images: statistical contour validation”, Digital Imaging Sensors and Applications, Imaging Science and Technology/SPIE, 21st Annual Symposium on Electronic Imaging, 2009.Google Scholar
  5. 5.
    Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras and Aggelos K. Katsaggelos, “Hybrid image segmentation using watersheds and fast region merging”, IEEE Transactions on Image Processing, vol. 7, pp. 1684–1699, 1998Google Scholar
  6. 6.
    Lifeng Liu and Stan Sclaro, “Shape-Guided Split and Merge of Image Regions”, 4th International Workshop on Visual Form, vol. 2059, pp. 367–377, 2001Google Scholar
  7. 7.
    Theo Pavlidis and Yuh-Tay Liow, “Integrating region growing and edge detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 225– 233, 1990CrossRefGoogle Scholar
  8. 8.
    Utpal Garain, Thierry Paquet, Laurent Heutte, “On foreground — background separation in low quality document images”, International Journal on Document Analysis and Recognition, vol. 8, pp. 47–63, 2006CrossRefGoogle Scholar
  9. 9.
    Yi Lu and Hong Guo, “Background Removal in Image indexing and Retrieval”, 10th International Conference on Image Analysis and Processing, pp. 933, 1999Google Scholar
  10. 10.
    Nicolas Coudray, Jean-Luc Buessler, Hubert Kihl, Jean-Philippe Urban, “Automated image analysis for electron microscopy specimen assessment”, 15th EUropean SIgnal Processing COnference (EUSIPCO), pp. 120–124, 2007Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • A. Karathanou
    • 1
  • J.-L. Buessler
    • 1
  • H. Kihl
    • 1
  • J.-P. Urban
    • 1
  1. 1.MIPS laboratoryUniversity of Haute Alsace, 4 rue des Frères LumièreMulhouse CedexFrance

Personalised recommendations