@inproceedings{427bef138ac641f9af3254dd7539e0fb,
title = "Fast and Robust Multi-Modal Image Registration for 3D Knee Kinematics",
abstract = "The process of spatially aligning two or more images acquired from different devices or imaging protocols is known as multi-modal image registration. As the similarity measure used is one of the most significant aspects of this process, certain measures have been proposed to enhance multi-modal image registration. However, the currently available measures are either not sufficiently accurate or are very computationally expensive. In this paper, a new hybrid multimodal registration approach is proposed. The new approach combines a fast measure, based on matching image edges, with a robust, but slow measure, which uses the joint probability distribution of the two images to be registered. Our experimental results reveal that using this hybrid approach provides a performance equivalent to the previously best measures but with a significantly reduced computational time.",
keywords = "edge detection, image registration, medical images, similarity measure, sum-of-conditional variance",
author = "Shabnam Saadat and Pickering, {Mark R.} and Diana Perriman and Scarvell, {Jennie M.} and Smith, {Paul N.}",
year = "2017",
month = dec,
day = "19",
doi = "10.1109/DICTA.2017.8227434",
language = "English",
isbn = "9781538628409",
series = "DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications",
publisher = "IEEE, Institute of Electrical and Electronics Engineers",
pages = "1--5",
editor = "Yi Guo",
booktitle = "DICTA 2017 - 2017 International Conference on Digital Image Computing",
address = "United States",
note = "International Conference on Digital Image Computing: Techniques and Applications 2017, DICTA 2017 ; Conference date: 29-11-2017 Through 01-12-2017",
}