Automatic best reference slice selection for smooth volume reconstruction of a mouse brain from histological images

Ula Baǧci; Li Bai

doi:10.1109/TMI.2010.2050594

Automatic best reference slice selection for smooth volume reconstruction of a mouse brain from histological images

Ula Baǧci^*, Li Bai

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

32 Scopus citations

Abstract

In this paper, we present a novel and effective method for registering histological slices of a mouse brain to reconstruct a 3-D volume. First, intensity variations in images are corrected through an intensity standardization process so that intensity values remain constant across slices. Second, the image space is transformed to a feature space where continuous variables are taken as high fidelity image features for accurate registration. Third, in order to improve the quality of the reconstructed volume, an automatic best reference slice selection algorithm is developed based on iterative assessment of image entropy and mean square error of the registration process. Fourth, a novel metric for evaluating the quality of the reconstructed volume is developed. Finally, the effect of optimal reference slice selection on the quality of registration and subsequent reconstruction is demonstrated.

Original language	English (US)
Article number	5484596
Pages (from-to)	1688-1696
Number of pages	9
Journal	IEEE Transactions on Medical Imaging
Volume	29
Issue number	9
DOIs	https://doi.org/10.1109/TMI.2010.2050594
State	Published - Sep 2010
Externally published	Yes

Funding

Manuscript received April 13, 2010; accepted May 07, 2010. Date of publication June 14, 2010; date of current version September 01, 2010. This work was supported by the European Commission Fp6 Marie Curie Action Programme (MEST-CT-2005-021170) under the CMIAG (Collaborative Medical Image Analysis on Grid) project. Asterisk indicates corresponding author. *U. Bag˘cı is with the School of Computer Science, University of Nottingham, NG8 1BB Nottingham, U.K. (e-mail: [email protected]). L. Bai is with the School of Computer Science, University of Nottingham, NG8 1BB Nottingham, U.K.. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2010.2050594

Keywords

Best reference slice
brain sectioning
histology images
image registration
intensity standardization
reference slice selection
smooth volume reconstruction

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TMI.2010.2050594

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abstract = "In this paper, we present a novel and effective method for registering histological slices of a mouse brain to reconstruct a 3-D volume. First, intensity variations in images are corrected through an intensity standardization process so that intensity values remain constant across slices. Second, the image space is transformed to a feature space where continuous variables are taken as high fidelity image features for accurate registration. Third, in order to improve the quality of the reconstructed volume, an automatic best reference slice selection algorithm is developed based on iterative assessment of image entropy and mean square error of the registration process. Fourth, a novel metric for evaluating the quality of the reconstructed volume is developed. Finally, the effect of optimal reference slice selection on the quality of registration and subsequent reconstruction is demonstrated.",

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note = "Funding Information: Manuscript received April 13, 2010; accepted May 07, 2010. Date of publication June 14, 2010; date of current version September 01, 2010. This work was supported by the European Commission Fp6 Marie Curie Action Programme (MEST-CT-2005-021170) under the CMIAG (Collaborative Medical Image Analysis on Grid) project. Asterisk indicates corresponding author. *U. Bag˘cı is with the School of Computer Science, University of Nottingham, NG8 1BB Nottingham, U.K. (e-mail: [email protected]). L. Bai is with the School of Computer Science, University of Nottingham, NG8 1BB Nottingham, U.K.. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2010.2050594",

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Automatic best reference slice selection for smooth volume reconstruction of a mouse brain from histological images

Abstract

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Keywords

ASJC Scopus subject areas

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