Literature DB >> 23078150

Automatic section thickness determination using an absolute gradient focus function.

D T Elozory1, K A Kramer, B Chaudhuri, O P Bonam, D B Goldgof, L O Hall, P R Mouton.   

Abstract

Quantitative analysis of microstructures using computerized stereology systems is an essential tool in many disciplines of bioscience research. Section thickness determination in current nonautomated approaches requires manual location of upper and lower surfaces of tissue sections. In contrast to conventional autofocus functions that locate the optimally focused optical plane using the global maximum on a focus curve, this study identified by two sharp 'knees' on the focus curve as the transition from unfocused to focused optical planes. Analysis of 14 grey-scale focus functions showed, the thresholded absolute gradient function, was best for finding detectable bends that closely correspond to the bounding optical planes at the upper and lower tissue surfaces. Modifications to this function generated four novel functions that outperformed the original. The 'modified absolute gradient count' function outperformed all others with an average error of 0.56 μm on a test set of images similar to the training set; and, an average error of 0.39 μm on a test set comprised of images captured from a different case, that is, different staining methods on a different brain region from a different subject rat. We describe a novel algorithm that allows for automatic section thickness determination based on just out-of-focus planes, a prerequisite for fully automatic computerized stereology.
© 2012 The Authors Journal of Microscopy © 2012 Royal Microscopical Society.

Entities:  

Mesh:

Year:  2012        PMID: 23078150      PMCID: PMC4465598          DOI: 10.1111/j.1365-2818.2012.03669.x

Source DB:  PubMed          Journal:  J Microsc        ISSN: 0022-2720            Impact factor:   1.758


  8 in total

1.  Comparison of autofocus methods for automated microscopy.

Authors:  L Firestone; K Cook; K Culp; N Talsania; K Preston
Journal:  Cytometry       Date:  1991

2.  Autofocusing in computer microscopy: selecting the optimal focus algorithm.

Authors:  Yu Sun; Stefan Duthaler; Bradley J Nelson
Journal:  Microsc Res Tech       Date:  2004-10       Impact factor: 2.769

3.  An automated microscope for cytologic research a preliminary evaluation.

Authors:  J F Brenner; B S Dew; J B Horton; T King; P W Neurath; W D Selles
Journal:  J Histochem Cytochem       Date:  1976-01       Impact factor: 2.479

4.  Autofocusing for automated microscopic evaluation of blood smear and pap smear.

Authors:  X Y Liu; W H Wang; Y Sun
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

5.  Digital differential interference contrast autofocus for high-resolution oil-immersion microscopy.

Authors:  Feimo Shen; Louis Hodgson; Jeffrey H Price; Klaus M Hahn
Journal:  Cytometry A       Date:  2008-07       Impact factor: 4.355

6.  Evaluation of autofocus functions in molecular cytogenetic analysis.

Authors:  A Santos; C Ortiz de Solórzano; J J Vaquero; J M Peña; N Malpica; F del Pozo
Journal:  J Microsc       Date:  1997-12       Impact factor: 1.758

7.  A comparison of different focus functions for use in autofocus algorithms.

Authors:  F C Groen; I T Young; G Ligthart
Journal:  Cytometry       Date:  1985-03

8.  Automated focusing in bright-field microscopy for tuberculosis detection.

Authors:  O A Osibote; R Dendere; S Krishnan; T S Douglas
Journal:  J Microsc       Date:  2010-11       Impact factor: 1.758

  8 in total
  5 in total

1.  Unbiased estimation of cell number using the automatic optical fractionator.

Authors:  Peter R Mouton; Hady Ahmady Phoulady; Dmitry Goldgof; Lawrence O Hall; Marcia Gordon; David Morgan
Journal:  J Chem Neuroanat       Date:  2016-12-14       Impact factor: 3.052

2.  Early Expression of Parkinson's Disease-Related Mitochondrial Abnormalities in PINK1 Knockout Rats.

Authors:  Lance M Villeneuve; Phillip R Purnell; Michael D Boska; Howard S Fox
Journal:  Mol Neurobiol       Date:  2014-11-25       Impact factor: 5.590

3.  Technical Note: Measuring the thickness of histological sections by detecting fluorescence intensity of embedding foam.

Authors:  David Ibsen Dadash-Khanlou; Benedicte Heegaard; Henrik Holten-Rossing; Thomas Hartvig Lindkær Jensen
Journal:  J Pathol Inform       Date:  2022-08-01

Review 4.  Basic quantitative morphological methods applied to the central nervous system.

Authors:  Lutz Slomianka
Journal:  J Comp Neurol       Date:  2020-08-01       Impact factor: 3.215

5.  Practicable methods for histological section thickness measurement in quantitative stereological analyses.

Authors:  Cyrill Matenaers; Bastian Popper; Alexandra Rieger; Rüdiger Wanke; Andreas Blutke
Journal:  PLoS One       Date:  2018-02-14       Impact factor: 3.240

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.