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3D Segmentation of Mammospheres for Localization Studies

  • Ju Han
  • Hang Chang
  • Qing Yang
  • Mary Helen Barcellos-Hoff
  • Bahram Parvin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

Three dimensional cell culture assays have emerged as the basis of an improved model system for evaluating therapeutic agents, molecular probes, and exogenous stimuli. However, there is a gap in robust computational techniques for segmentation of image data that are collected through confocal or deconvolution microscopy. The main issue is the volume of data, overlapping subcellular compartments, and variation in scale and size of subcompartments of interest. A geometric technique has been developed to bound the solution of the problem by first localizing centers of mass for each cell and then partitioning clump of cells along minimal intersecting surfaces. An approximate solution to the center of mass is realized through iterative spatial voting, which is tolerant to variation in shape morphologies and overlapping compartments and is shown to have an excellent noise immunity. These centers of mass are then used to partition a clump of cells along minimal intersecting surfaces that are estimated by Radon transform. Examples on real data and performance of the system over a large population of data are evaluated. Although proposed strategies have been developed and tested on data collected through fluorescence microscopy, they are applicable to other problems in low level vision and medical imaging.

Keywords

Nuclear Region Radial Symmetry Voronoi Tessellation Neighboring Nucleus Coarse Segmentation 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ju Han
    • 1
  • Hang Chang
    • 1
    • 2
  • Qing Yang
    • 2
  • Mary Helen Barcellos-Hoff
    • 1
  • Bahram Parvin
    • 1
  1. 1.Lawrence Berkeley National LaboratoryBerkeley
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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