Statistical inference of biological structure and point spread functions in 3D microscopy

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

2 Citations (Scopus)

Abstract

We present a novel method for detecting and quantifying 3D structure in stacks of microscopic images captured at incremental focal lengths. We express the image data as stochastically generated by an underlying model for biological specimen and the effects of the imaging system. The method simultaneously fits a model for proposed structure and the imaging system's parameters, which include a model of the point spread function. We demonstrate our approach by detecting spores in image stacks of Alternaria, a microscopic genus of fungus. The spores are modeled as opaque ellipsoids and fit to the data using statistical inference. Since the number of spores in the data is not known, model selection is incorporated into the fitting process. Thus, we develop a reversible jump Markov chain Monte Carlo sampler to explore the parameter space. Our results show that simultaneous statistical inference of specimen and imaging models is useful for quantifying biological structures in 3D microscopic images. In addition, we show that inferring a model of the imaging system improves the overall fit of the specimen model to the data.

Original languageEnglish (US)
Title of host publicationProceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006
Pages373-380
Number of pages8
DOIs
StatePublished - 2007
Event3rd International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 - Chapel Hill, NC, United States
Duration: Jun 14 2006Jun 16 2006

Other

Other3rd International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006
CountryUnited States
CityChapel Hill, NC
Period6/14/066/16/06

Fingerprint

Optical transfer function
Microscopic examination
Imaging systems
Fungi
Markov processes
Imaging techniques

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications

Cite this

Schlecht, J., Barnard, J. J., & Pryor, B. M. (2007). Statistical inference of biological structure and point spread functions in 3D microscopy. In Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006 (pp. 373-380). [4155750] https://doi.org/10.1109/3DPVT.2006.131

Statistical inference of biological structure and point spread functions in 3D microscopy. / Schlecht, Joseph; Barnard, Jacobus J; Pryor, Barry M.

Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006. 2007. p. 373-380 4155750.

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

Schlecht, J, Barnard, JJ & Pryor, BM 2007, Statistical inference of biological structure and point spread functions in 3D microscopy. in Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006., 4155750, pp. 373-380, 3rd International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006, Chapel Hill, NC, United States, 6/14/06. https://doi.org/10.1109/3DPVT.2006.131
Schlecht J, Barnard JJ, Pryor BM. Statistical inference of biological structure and point spread functions in 3D microscopy. In Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006. 2007. p. 373-380. 4155750 https://doi.org/10.1109/3DPVT.2006.131
Schlecht, Joseph ; Barnard, Jacobus J ; Pryor, Barry M. / Statistical inference of biological structure and point spread functions in 3D microscopy. Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006. 2007. pp. 373-380
@inproceedings{94561034b9d9473989bb0d67f549929c,
title = "Statistical inference of biological structure and point spread functions in 3D microscopy",
abstract = "We present a novel method for detecting and quantifying 3D structure in stacks of microscopic images captured at incremental focal lengths. We express the image data as stochastically generated by an underlying model for biological specimen and the effects of the imaging system. The method simultaneously fits a model for proposed structure and the imaging system's parameters, which include a model of the point spread function. We demonstrate our approach by detecting spores in image stacks of Alternaria, a microscopic genus of fungus. The spores are modeled as opaque ellipsoids and fit to the data using statistical inference. Since the number of spores in the data is not known, model selection is incorporated into the fitting process. Thus, we develop a reversible jump Markov chain Monte Carlo sampler to explore the parameter space. Our results show that simultaneous statistical inference of specimen and imaging models is useful for quantifying biological structures in 3D microscopic images. In addition, we show that inferring a model of the imaging system improves the overall fit of the specimen model to the data.",
author = "Joseph Schlecht and Barnard, {Jacobus J} and Pryor, {Barry M}",
year = "2007",
doi = "10.1109/3DPVT.2006.131",
language = "English (US)",
isbn = "0769528252",
pages = "373--380",
booktitle = "Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006",

}

TY - GEN

T1 - Statistical inference of biological structure and point spread functions in 3D microscopy

AU - Schlecht, Joseph

AU - Barnard, Jacobus J

AU - Pryor, Barry M

PY - 2007

Y1 - 2007

N2 - We present a novel method for detecting and quantifying 3D structure in stacks of microscopic images captured at incremental focal lengths. We express the image data as stochastically generated by an underlying model for biological specimen and the effects of the imaging system. The method simultaneously fits a model for proposed structure and the imaging system's parameters, which include a model of the point spread function. We demonstrate our approach by detecting spores in image stacks of Alternaria, a microscopic genus of fungus. The spores are modeled as opaque ellipsoids and fit to the data using statistical inference. Since the number of spores in the data is not known, model selection is incorporated into the fitting process. Thus, we develop a reversible jump Markov chain Monte Carlo sampler to explore the parameter space. Our results show that simultaneous statistical inference of specimen and imaging models is useful for quantifying biological structures in 3D microscopic images. In addition, we show that inferring a model of the imaging system improves the overall fit of the specimen model to the data.

AB - We present a novel method for detecting and quantifying 3D structure in stacks of microscopic images captured at incremental focal lengths. We express the image data as stochastically generated by an underlying model for biological specimen and the effects of the imaging system. The method simultaneously fits a model for proposed structure and the imaging system's parameters, which include a model of the point spread function. We demonstrate our approach by detecting spores in image stacks of Alternaria, a microscopic genus of fungus. The spores are modeled as opaque ellipsoids and fit to the data using statistical inference. Since the number of spores in the data is not known, model selection is incorporated into the fitting process. Thus, we develop a reversible jump Markov chain Monte Carlo sampler to explore the parameter space. Our results show that simultaneous statistical inference of specimen and imaging models is useful for quantifying biological structures in 3D microscopic images. In addition, we show that inferring a model of the imaging system improves the overall fit of the specimen model to the data.

UR - http://www.scopus.com/inward/record.url?scp=47249089156&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=47249089156&partnerID=8YFLogxK

U2 - 10.1109/3DPVT.2006.131

DO - 10.1109/3DPVT.2006.131

M3 - Conference contribution

AN - SCOPUS:47249089156

SN - 0769528252

SN - 9780769528250

SP - 373

EP - 380

BT - Proceedings - Third International Symposium on 3D Data Processing, Visualization, and Transmission, 3DPVT 2006

ER -