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 journalArticlepeer-review

27 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 languageEnglish (US)
Article number5484596
Pages (from-to)1688-1696
Number of pages9
JournalIEEE Transactions on Medical Imaging
Volume29
Issue number9
DOIs
StatePublished - Sep 2010
Externally publishedYes

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

Fingerprint Dive into the research topics of 'Automatic best reference slice selection for smooth volume reconstruction of a mouse brain from histological images'. Together they form a unique fingerprint.

Cite this