- Title
- A detector of structural similarity for multi-modal microscopic image registration
- Creator
- Lv, Guohua; Teng, Shyh; Lu, Guojun
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/164218
- Identifier
- vital:13012
- Identifier
-
https://doi.org/10.1007/s11042-017-4669-y
- Identifier
- ISBN:1380-7501
- Abstract
- This paper presents a Detector of Structural Similarity (DSS) to minimize the visual differences between brightfield and confocal microscopic images. The context of this work is that it is very challenging to effectively register such images due to a low structural similarity in image contents. To address this issue, DSS aims to maximize the structural similarity by utilizing the intensity relationships among red-green-blue (RGB) channels in images. Technically, DSS can be combined with any multi-modal image registration technique in registering brightfield and confocal microscopic images. Our experimental results show that DSS significantly increases the visual similarity in such images, thereby improving the registration performance of an existing state-of-the-art multi-modal image registration technique by up to approximately 27%. © 2017, Springer Science+Business Media New York.
- Publisher
- Springer New York LLC
- Relation
- Multimedia Tools and Applications Vol. 77, no. 6 (2018), p. 7675-7701
- Rights
- Copyright © 2017, Springer Science+Business Media New York.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0803 Computer Software; 0806 Information Systems; Image registration; Multi-modal microscopic images; Structural similarity
- Reviewed
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