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Self-learning Segmentation and Classification of Cell-Nuclei in 3D Volumetric Data Using
 

Summary: Self-learning Segmentation and Classification of
Cell-Nuclei in 3D Volumetric Data Using
Voxel-Wise Gray Scale Invariants
Janis Fehr1
, Olaf Ronneberger1
, Haymo Kurz2
, and Hans Burkhardt1
1
Albert-Ludwigs-Universit¨at Freiburg, Institut f¨ur Informatik,
Lehrstuhl f¨ur Mustererkennung und Bildverarbeitung,
Georges-Koehler-Allee Geb. 052, 79110 Freiburg, Deutschland
fehr@informatik.uni-freiburg.de
http://lmb.informatik.uni-freiburg.de/
2
Albert-Ludwigs-Universit¨at Freiburg, Institut f¨ur Anatomie und Zell Biologie,
79104 Feiburg i.Br., Deutschland
Abstract. We introduce and discuss a new method for segmentation
and classification of cells from 3D tissue probes. The anisotropic 3D
volumetric data of fluorescent marked cell nuclei is recorded by a confo-
cal laser scanning microscope (LSM). Voxel-wise gray scale features (see

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung

 

Collections: Computer Technologies and Information Sciences