3D Segmentation of Mammospheres for Localization Studies
Int. Symp. on Visual Computing, 2006
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.
here to see the full version of the paper in Acrobat format