Share Email Print
cover

Proceedings Paper

A statistical deformation model (SDM) based regularizer for non-rigid image registration: application to registration of multimodal prostate MRI and histology
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Free form deformation (FFD) is a popular algorithm for non-linear image registration because of its ability to accurately recover deformations. However, due to the unconstrained nature of elastic registration, FFD may introduce unrealistic deformations, especially when differences between template and target image are large, thereby necessitating a regularizer to constrain the registration to a physically meaning transformation. Prior knowledge in the form of a Statistical Deformation Model (SDM) in a registration scheme has been shown to function as an effective regularizer. With a similar underlying premise, in this paper, we present a novel regularizer for FFD that leverages knowledge of known, valid deformations to train a statistical deformation model (SDM). At each iteration of the FFD registration, the SDM is utilized to calculate the likelihood of a given deformation occurring and appropriately influence the similarity metric to limit the registration to only realistic deformations. We quantitatively evaluate robustness of the SDM regularizer in the framework of FFD through a set of synthetic experiments using brain images with a range of induced deformations and 3 types of multiplicative noise - Gaussian, salt and pepper and speckle. We demonstrate that FFD with the inclusion of the SDM regularizer yields up to a 19% increase in normalized cross correlation (NCC) and a 16% decrease in root mean squared (RMS) error and mean absolute distance (MAD). Registration performance was also evaluated qualitatively and quantitatively in spatially aligning ex vivo pseudo whole mount histology (WMH) sections and in vivo prostate MRI in order to map the spatial extent of prostate cancer (CaP) onto corresponding radiologic imaging. Across all evaluation measures (MAD, RMS, and DICE), regularized FFD performed significantly better compared to unregularized FFD.

Paper Details

Date Published: 15 March 2013
PDF: 13 pages
Proc. SPIE 8676, Medical Imaging 2013: Digital Pathology, 86760C (15 March 2013); doi: 10.1117/12.2008707
Show Author Affiliations
Srivathsan Babu Prabu, Rutgers, The State Univ. of New Jersey (United States)
Robert Toth, Rutgers, The State Univ. of New Jersey (United States)
Anant Madabhushi, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 8676:
Medical Imaging 2013: Digital Pathology
Metin N. Gurcan; Anant Madabhushi, Editor(s)

© SPIE. Terms of Use
Back to Top