Multi-Modal Medical Image Registration with Full or Partial Data: A Manifold Learning Approach
Open accessAbstract
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.
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Multimodal image alignment provides spatial mapping between images taken by sensors of different modalities and can be categorized into feature-based and patch-based methods.
“Specifically, multimodal image alignment provides a spatial mapping capability between images taken by sensors of different modalities, which may be categorized into feature-based [167, 168] and patch-based [165, 166] methods.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [166]
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- Authors
- Bashiri, F. S., Baghaie, A., Rostami, R., Yu, Z., & D’Souza, R. M.
- Affiliations
- University of Wisconsin–Milwaukee (US)
- Corresponding author
- Bashiri, Fereshteh S.
- Yu, Zeyun
- D’Souza, Roshan M.
- Venue
- Journal of Imaging
- Publisher
- MDPI AG
- Published
- 2018-12-30
- Volume
- 5
- Issue
- 1
- Pages
- 5
- ISSN
- 2313-433X (ISSN-L)
- PMC
- PMC8320870
- OpenAlex
- W2908425794
- Cited by
- 46
- References
- 81
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 7 · 2023: 6 · 2022: 9 · 2021: 5 · 2020: 13 · 2019: 2
- FWCI
- 2.50
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Journal of Imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- GE Healthcare through UWM Catalyst Grant Program (2010-11-114-6100-10093)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- multimodal image registration, manifold learning, partial overlap, CT-MRI registration, brain MRI
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