Abstract
3D reconstruction and digital double staining offer pathologists many new insights into tissue structure and metabolism. Key to these applications is the precise registration of histological slide images, that is challenging in several ways. One major challenge are differently stained slides, that highlight different parts of the tissue. In this paper we introduce a new registration method to face this multimodality. It abstracts the image information to cell nuclei densities. By minimizing the distance of these densities an affine transformation is determined that restores the lost spatial correspondences. The proposed density based registration is evaluated using consecutive histological slides. It is compared to a Mutual Information based registration and shown to be more accurate and robust.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ourselin S, Roche A, Subsol G. Reconstructing a 3D structure from serial histological sections. Image Vis Comput. 2001;19(1):25–31.
Lotz J, Berger J, M¨uller B, et al. Zooming in: high resolution 3D reconstruction of differently stained histological whole slide images. Proc SPIE. 2014;9041:1–7.
Braumann U, Scherf N, Einenkel J, et al. Large histological serial sections for computational tissue volume reconstruction. Methods Inf Med. 2007;46(5).
Schwier M, Hahn H, Dirsch O, et al. Registration of histological whole slide images guided by vessel structures. J Path Inform. 2013;4(10).
Song Y, Treanor D, Bulpitt A, et al. 3D reconstruction of multiple stained histology images. J Path Inform. 2013;4(7).
Lotz J, Weiss N, Modersitzki J. Efficient whole slide image registration based on nuclei densities; 2015. In preperation.
Homeyer A, Schenk A, Arlt J, et al. Practical quantification of necrosis in histological whole-slide images. Comput Med Imag Grap. 2013;37(4):313–22.
Silverman B. Density Estimation. CRC Press; 1986.
Tsin Y, Kanade T. A correlation-based approach to robust point set registration. Eur Conf Computer Vis. 2004;3023:558–69.
Modersitzki J. FAIR: Flexible Algorithms for Image Registration. SIAM; 2009.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Weiss, N., Lotz, J., Modersitzki, J. (2015). Multimodal Image Registration in Digital Pathology Using Cell Nuclei Densities. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-662-46224-9_43
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-46223-2
Online ISBN: 978-3-662-46224-9
eBook Packages: Computer Science and Engineering (German Language)