Cellular recurrent deep network for image registration

M. Alam; L. Vidyaratne; Khan M. Iftekharuddin

doi:10.1117/12.2197658

9 September 2015 Cellular recurrent deep network for image registration

M. Alam, L. Vidyaratne, Khan M. Iftekharuddin

Proceedings Volume 9598, Optics and Photonics for Information Processing IX; 95981B (2015) https://doi.org/10.1117/12.2197658
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

Abstract

Image registration using Artificial Neural Network (ANN) remains a challenging learning task. Registration can be posed as a two-step problem: parameter estimation and actual alignment/transformation using the estimated parameters. To date ANN based image registration techniques only perform the parameter estimation, while affine equations are used to perform the actual transformation. In this paper, we propose a novel deep ANN based image rigid registration that combines parameter estimation and transformation as a simultaneous learning task. Our previous work shows that a complex universal approximator known as Cellular Simultaneous Recurrent Network (CSRN) can successfully approximate affine transformations with known transformation parameters. This study introduces a deep ANN that combines a feed forward network with a CSRN to perform full rigid registration. Layer wise training is used to pre-train feed forward network for parameter estimation and followed by a CSRN for image transformation respectively. The deep network is then fine-tuned to perform the final registration task. Our result shows that the proposed deep ANN architecture achieves comparable registration accuracy to that of image affine transformation using CSRN with known parameters. We also demonstrate the efficacy of our novel deep architecture by a performance comparison with a deep clustered MLP.

Citation Download Citation

M. Alam, L. Vidyaratne, and Khan M. Iftekharuddin "Cellular recurrent deep network for image registration", Proc. SPIE 9598, Optics and Photonics for Information Processing IX, 95981B (9 September 2015); https://doi.org/10.1117/12.2197658

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