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Fast Scalar and Vectorial Grayscale Based Invariant Features for 3D Cell Nuclei Localization and Classification

  • Janina Schulz
  • Thorsten Schmidt
  • Olaf Ronneberger
  • Hans Burkhardt
  • Taras Pasternak
  • Alexander Dovzhenko
  • Klaus Palme
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)

Abstract

Since biology and medicine apply increasingly fast volumetric imaging techniques and aim at extracting quantitative data from these images, the need for efficient image analysis techniques like detection and classification of 3D structures is obvious. A common approach is to extract local features, e.g. group integration has been used to gain invariance against rotation and translation. We extend these group integration features by including vectorial information and spherical harmonics descriptors. From our vectorial invariants we derive a very robust detector for spherical structures in low-quality images and show that it can be computed very fast. We apply these new invariants to 3D confocal laser-scanning microscope images of the Arabidopsis root tip and extract position and type of the cell nuclei. Then it is possible to build a biologically relevant, architectural model of the root tip.

Keywords

Kernel Function Cell Nucleus Invariant Feature Rotation Matrice Group Integration 
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.

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References

  1. 1.
    Schulz-Mirbach, H.: Invariant features for gray scale images. In: DAGM-Symposium, Bielefeld, Germany (1995)Google Scholar
  2. 2.
    Ronneberger, O., et al.: General-purpose object recognition in 3D volume data sets using gray-scale invariants. In: ICPR, Quebec, Canada (2002)Google Scholar
  3. 3.
    Fehr, J., et al.: Self-learning segmentation and classification of cell-nuclei in 3D volumetric data using voxel-wise gray-scale invariants. In: DAGM-Symposium, Vienna, Austria (2005)Google Scholar
  4. 4.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. of Computer Vision 60, 2 (2004)Google Scholar
  5. 5.
    Bao, Z., et al.: Automated cell lineage tracing in caenorhabditis elegans. In: PNAS (2006)Google Scholar
  6. 6.
    Wirjadi, O., et al.: Automated feature selection for the classification of meningioma cell nuclei. Bildverarbeitung für die Medizin (2006)Google Scholar
  7. 7.
    Reisert, M., et al.: General purpose invariant 3D features based on group integration using directional information and spherical harmonic expansion. In: ICPR, Hong Kong (2006)Google Scholar
  8. 8.
    Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Pattern Recognition 13(2) (1981)Google Scholar
  9. 9.
    Kazhdan, M., et al.: Rotation invariant spherical harmonic representation of 3d shape descriptors. In: Symp. on Geom. Process. (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Janina Schulz
    • 1
  • Thorsten Schmidt
    • 1
  • Olaf Ronneberger
    • 1
  • Hans Burkhardt
    • 1
  • Taras Pasternak
    • 2
  • Alexander Dovzhenko
    • 2
  • Klaus Palme
    • 2
  1. 1.Institut für Informatik, Lehrstuhl für Mustererkennung und BildverarbeitungAlbert-Ludwigs-Universität Freiburg 
  2. 2.Institut für Biologie II, BotanikAlbert-Ludwigs-Universität Freiburg 

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