Title
Model Based Approach for Improved 3-D Segmentation of Aggregated-Nuclei in Confocal Image Stacks
Abstract
We present a study on a new modeling based approach to cell segmentation and its application to cell detection in 3D space. As a model, we use an ellipsoid to which three types of deformation can be applied, namely translation, rotation, and scaling. The geometric and intensity parameters of the models are optimized independently at each iteration step. The purpose of this approach is to derive a virtual ""fitting"" Pseudo-Force that drives the ellipsoid to the apparent boundaries of the cell. In an environment where a number of cells can coexist and partially overlap, it is also necessary to model the inter-object constraints that limit the extent to which overlap is possible. We propose to implement this constraint as an ""interaction"" force that will counteract the ""fitting"" force and prevent two objects from occupying the same space.
Keywords
3-D Segmentation, Aggregated-Nuclei
Subject Categories
Three-dimensional imaging
Disciplines
Engineering
Publisher
Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)
Publication Date
2007
Rights Holder
Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS)
Permanent URL
Recommended Citation
Roussel, Nicolas; Tyrell, James A.; Shen, Qin; Temple, Sally; and Roysam, Badrinath, "Model Based Approach for Improved 3-D Segmentation of Aggregated-Nuclei in Confocal Image Stacks" (2007). Research Thrust R2 Presentations. Paper 28. http://hdl.handle.net/2047/d10008895
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Notes
Poster presented at the 2007 Thrust R2B Localized Probing and Mosaicing Methods Conference