A novel learning-based dissimilarity metric for rigid and non-rigid medical image registration by using bhattacharyya distances
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So et al. proposed a novel learning-based metric using Bhattacharyya Distances, where dissimilarity of testing image pairs is calculated by incorporating expected intensity distributions learned from registered training image pairs.
“So et al. (57) proposed a novel learning-based metric by using Bhattacharyya Distances. The dissimilarity of the testing image pairs is calculated by incorporating the expected intensity distributions learned from the registered training image pairs.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [57]
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- Authors
- So, R. W. K., & Chung, A. C. S.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Corresponding author
- So, Ronald W.K.
- ORCID
- Chung, Albert C.S.
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 62
- Pages
- 161-174
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2519648241
- Cited by
- 36
- References
- 39
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 1 · 2023: 1 · 2022: 7 · 2021: 8 · 2020: 3 · 2019: 4 · 2018: 3 · 2017: 2
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- medical image registration, dissimilarity measures, Bhattacharyya distance, rigid registration, non-rigid registration, multimodal image registration
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