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Proceedings Paper

Local structure orientation: a new method for histology and MRI coregistration
Author(s): Wadha Alyami; Andre Kyme; Roger Bourne
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Paper Abstract

Histology-based validation of magnetic resonance imaging (MRI) is dependent on spatial alignment of MRI and histology data obtained by very different physical techniques that usually generate very different types of image contrast. Most commonly, landmarks and/or mutual information (MI) techniques are employed for image coregistration. Landmark methods are limited by changed appearance or saliency of features manually selected in corresponding images. MI methods assume that pixel intensity distributions follow consistent relationships, however this assumption is often violated when differences exist in image texture and appearance. We propose a new approach based on local structure orientation (LSO). LSO-registration uses a tissue microstructure feature, fibre orientation, that can be measured by both histology and MRI, providing a dense feature field for an objective coregistration process. This structure feature, in-plane fibre orientation angle, can be generated from a histological image using a structure tensor analysis algorithm, and from 3D or 2D diffusion tensor MRI (DTI). We present a preliminary validation of LSO-registration based on digital and physical tissue structure phantoms that simulate fibre orientation in a human prostate. Using Elastix and Transformix we compare the performance of mutual information (MI), advanced mean squares (AMS), and a modified AMS method (AMSR) that corrects for angular redundancies. Registration accuracy was assessed quantitatively based on correction of deformed grid points and fiducial markers. Residual errors after registration using the physical tissue phantom with fiducial markers were: MI (3.4 ± 0.80), AMS (12.2 ± 5.7) and AMSR (2.1 ± 0.5).

Paper Details

Date Published: 10 March 2020
PDF: 11 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113132W (10 March 2020); doi: 10.1117/12.2549194
Show Author Affiliations
Wadha Alyami, The Univ. of Sydney (Australia)
Andre Kyme, The Univ. of Sydney (Australia)
Roger Bourne, The Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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