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J. Imaging 2019, 5(1), 5; https://doi.org/10.3390/jimaging5010005

Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach

1
Department of Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
2
Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
3
Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA
*
Authors to whom correspondence should be addressed.
Received: 20 September 2018 / Revised: 23 December 2018 / Accepted: 25 December 2018 / Published: 30 December 2018
(This article belongs to the Special Issue Medical Image Analysis)
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Abstract

Multi-modal image registration is the primary step in integrating information stored in two or more images, which are captured using multiple imaging modalities. In addition to intensity variations and structural differences between images, they may have partial or full overlap, which adds an extra hurdle to the success of registration process. In this contribution, we propose a multi-modal to mono-modal transformation method that facilitates direct application of well-founded mono-modal registration methods in order to obtain accurate alignment of multi-modal images in both cases, with complete (full) and incomplete (partial) overlap. The proposed transformation facilitates recovering strong scales, rotations, and translations. We explain the method thoroughly and discuss the choice of parameters. For evaluation purposes, the effectiveness of the proposed method is examined and compared with widely used information theory-based techniques using simulated and clinical human brain images with full data. Using RIRE dataset, mean absolute error of 1.37, 1.00, and 1.41 mm are obtained for registering CT images with PD-, T1-, and T2-MRIs, respectively. In the end, we empirically investigate the efficacy of the proposed transformation in registering multi-modal partially overlapped images. View Full-Text
Keywords: medical image registration; multi-modality; partially overlapped images; manifold learning medical image registration; multi-modality; partially overlapped images; manifold learning
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Bashiri, F.S.; Baghaie, A.; Rostami, R.; Yu, Z.; D’Souza, R.M. Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach. J. Imaging 2019, 5, 5.

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