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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 836202, 16 pages
http://dx.doi.org/10.1155/2015/836202
Research Article

Deep Adaptive Log-Demons: Diffeomorphic Image Registration with Very Large Deformations

College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China

Received 31 October 2014; Revised 28 December 2014; Accepted 28 December 2014

Academic Editor: Yi Gao

Copyright © 2015 Liya Zhao and Kebin Jia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper proposes a new framework for capturing large and complex deformation in image registration. Traditionally, this challenging problem relies firstly on a preregistration, usually an affine matrix containing rotation, scale, and translation and afterwards on a nonrigid transformation. According to preregistration, the directly calculated affine matrix, which is obtained by limited pixel information, may misregistrate when large biases exist, thus misleading following registration subversively. To address this problem, for two-dimensional (2D) images, the two-layer deep adaptive registration framework proposed in this paper firstly accurately classifies the rotation parameter through multilayer convolutional neural networks (CNNs) and then identifies scale and translation parameters separately. For three-dimensional (3D) images, affine matrix is located through feature correspondences by a triplanar 2D CNNs. Then deformation removal is done iteratively through preregistration and demons registration. By comparison with the state-of-the-art registration framework, our method gains more accurate registration results on both synthetic and real datasets. Besides, principal component analysis (PCA) is combined with correlation like Pearson and Spearman to form new similarity standards in 2D and 3D registration. Experiment results also show faster convergence speed.