@inproceedings{915c5ed5876d4a5f84d0497f1df42532,
title = "Image registration with optimal regularization parameter selection by learned auto encoder features",
abstract = "In this paper, we propose a method that optimizes a regularization parameter for the regularized Free Form Deformation (FFD) non-rigid image registration. The developed process utilizes autoencoder generated image representations to assess image data generalization quality by the regularization parameter. Both pixel intensity and learned features are used to improve the overall accuracy and regularity of the resulting inverse problem solution. We implement the new selection criterion with its use in the non-rigid image FFD registration based on multi-level Bspline with L2-regularization, and validate the method with synthetic and real histopathology image datasets. Both qualitative and quantitative results suggest the efficacy of our developed method for fine-tuning histopathology microscope images.",
keywords = "Autoencoder, Bspline, Free form deformation, Image registration, Inverse problems, Whole slide image",
author = "Aurelie Akossi and Fusheng Wang and George Teodoro and Jun Kong",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 ; Conference date: 13-04-2021 Through 16-04-2021",
year = "2021",
month = apr,
day = "13",
doi = "10.1109/ISBI48211.2021.9434161",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "702--705",
booktitle = "2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021",
}