Inferring grammar-based structure models from 3D microscopy data

Joseph Schlecht, Kobus Barnard, Ekaterina Spriggs, Barry Pryor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

We present a new method to fit grammar-based stochastic models for biological structure to stacks of microscopic images captured at incremental focal lengths. Providing the ability to quantitatively represent structure and automatically fit it to image data enables important biological research. We consider the case where individuals can be represented as an instance of a stochastic grammar, similar to L-systems used in graphics to produce realistic plant models. In particular, we construct a stochastic grammar of Alternaria, a genus of fungus, and fit instances of it to microscopic image stacks. We express the image data as the result of a generative process composed of the underlying probabilistic structure model together with the parameters of the imaging system. Fitting the model then becomes probabilistic inference. For this we create a reversible-jump MCMC sampler to traverse the parameter space. We observe that incorporating spatial structure helps fit the model parts, and that simultaneously fitting the imaging system is also very helpful.

Original languageEnglish (US)
Title of host publication2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs
StatePublished - Oct 11 2007
Event2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States
Duration: Jun 17 2007Jun 22 2007

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Other

Other2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
CountryUnited States
CityMinneapolis, MN
Period6/17/076/22/07

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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