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A detector of structural similarity for multi-modal microscopic image registration

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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%.

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Notes

  1. http://www.nikoninstruments.com/Learn-Explore/Techniques/Brightfield.

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Acknowledgements

We thank Dr. Mary Vail from Department of Biochemistry & Molecular Biology of Monash University for providing valuable information to accurately describe how brightfield and confocal microscopic images are captured.

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Authors and Affiliations

  1. School of Information, Qilu University of Technology, Jinan, China

    Guohua Lv

  2. Faculty of Science & Technology, Federation University Australia, Victoria, Australia

    Shyh Wei Teng & Guojun Lu

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  1. Guohua Lv
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  2. Shyh Wei Teng
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  3. Guojun Lu
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Correspondence to Guohua Lv.

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Lv, G., Teng, S.W. & Lu, G. A detector of structural similarity for multi-modal microscopic image registration. Multimed Tools Appl 77, 7675–7701 (2018). https://doi.org/10.1007/s11042-017-4669-y

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  • DOI: https://doi.org/10.1007/s11042-017-4669-y

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