MCMC-Based Algorithm to Adjust Scale
Bias in Large Series of Electron
Microscopical Ultrathin Sections

Huaizhong Zhang; Patricia Rodriguez; Philip Morrow; Sally McClean; Kurt Saetzler

MCMC-Based Algorithm to Adjust Scale Bias in Large Series of Electron Microscopical Ultrathin Sections

Huaizhong Zhang, Patricia Rodriguez, Philip Morrow, Sally McClean, Kurt Saetzler

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding (ISBN) › peer-review

Abstract

When using a non-rigid registration scheme, it is possible that bias is introduced during the registration process of consecutive sections. This bias can accumulate when large series of sections are to be registered and can cause substantial distortions of the scale space of individual sections thus leading to significant measurement bias. This paper presents an automated scheme based on Markov Chain Monte Carlo (MCMC) techniques to estimate and eliminate registration bias. For this purpose, a hierarchical model is used based on the assumption that (a) each section has the same, independent probability to be deformed by the sectioning and therefore the subsequent registration process and (b) the varying bias introduced by the registration process has to be balanced such that the average section area is preserved forcing the average scale parameters to have a mean value of 1.0.

Original language	English
Title of host publication	Not Known
Publication status	Published - 2 Sep 2009
Event	13th International CAIP Conference - University of Münster, Münster, Germany Duration: 2 Sep 2009 → 4 Sep 2009

Conference

Conference	13th International CAIP Conference
Country/Territory	Germany
City	Münster
Period	2/09/09 → 4/09/09

Access to Document

http://cvpr.uni-muenster.de/CAIP2009/cfp.html

Cite this

@inproceedings{b512eb4c7c2c45ed8c68be70bd7ae55b,

title = "MCMC-Based Algorithm to Adjust Scale Bias in Large Series of Electron Microscopical Ultrathin Sections",

abstract = "When using a non-rigid registration scheme, it is possible that bias is introduced during the registration process of consecutive sections. This bias can accumulate when large series of sections are to be registered and can cause substantial distortions of the scale space of individual sections thus leading to significant measurement bias. This paper presents an automated scheme based on Markov Chain Monte Carlo (MCMC) techniques to estimate and eliminate registration bias. For this purpose, a hierarchical model is used based on the assumption that (a) each section has the same, independent probability to be deformed by the sectioning and therefore the subsequent registration process and (b) the varying bias introduced by the registration process has to be balanced such that the average section area is preserved forcing the average scale parameters to have a mean value of 1.0.",

author = "Huaizhong Zhang and Patricia Rodriguez and Philip Morrow and Sally McClean and Kurt Saetzler",

note = "Computer Analysis of Images and Patterns; 13th International CAIP Conference ; Conference date: 02-09-2009 Through 04-09-2009",

year = "2009",

month = sep,

day = "2",

language = "English",

isbn = "978-3-642-03767-2",

booktitle = "Not Known",

}

TY - GEN

T1 - MCMC-Based Algorithm to Adjust Scale Bias in Large Series of Electron Microscopical Ultrathin Sections

AU - Zhang, Huaizhong

AU - Rodriguez, Patricia

AU - Morrow, Philip

AU - McClean, Sally

AU - Saetzler, Kurt

N1 - Computer Analysis of Images and Patterns

PY - 2009/9/2

Y1 - 2009/9/2

N2 - When using a non-rigid registration scheme, it is possible that bias is introduced during the registration process of consecutive sections. This bias can accumulate when large series of sections are to be registered and can cause substantial distortions of the scale space of individual sections thus leading to significant measurement bias. This paper presents an automated scheme based on Markov Chain Monte Carlo (MCMC) techniques to estimate and eliminate registration bias. For this purpose, a hierarchical model is used based on the assumption that (a) each section has the same, independent probability to be deformed by the sectioning and therefore the subsequent registration process and (b) the varying bias introduced by the registration process has to be balanced such that the average section area is preserved forcing the average scale parameters to have a mean value of 1.0.

AB - When using a non-rigid registration scheme, it is possible that bias is introduced during the registration process of consecutive sections. This bias can accumulate when large series of sections are to be registered and can cause substantial distortions of the scale space of individual sections thus leading to significant measurement bias. This paper presents an automated scheme based on Markov Chain Monte Carlo (MCMC) techniques to estimate and eliminate registration bias. For this purpose, a hierarchical model is used based on the assumption that (a) each section has the same, independent probability to be deformed by the sectioning and therefore the subsequent registration process and (b) the varying bias introduced by the registration process has to be balanced such that the average section area is preserved forcing the average scale parameters to have a mean value of 1.0.

M3 - Conference proceeding (ISBN)

SN - 978-3-642-03767-2

BT - Not Known

T2 - 13th International CAIP Conference

Y2 - 2 September 2009 through 4 September 2009

ER -

MCMC-Based Algorithm to Adjust Scale Bias in Large Series of Electron Microscopical Ultrathin Sections

Abstract

Conference

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