Fast and Robust Multi-Modal Image Registration for 3D Knee Kinematics

10Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.
Get full text

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.

References Powered by Scopus

A Computational Approach to Edge Detection

25182Citations
N/AReaders
Get full text

Image registration methods: A survey

5889Citations
N/AReaders
Get full text

A survey of medical image registration

2715Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Medical image fusion method by deep learning

260Citations
N/AReaders
Get full text

An efficient hybrid method for 3D to 2D medical image registration

7Citations
N/AReaders
Get full text

Object-Based Motion Estimation Using the EPD Similarity Measure

6Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Saadat, S., Pickering, M. R., Perriman, D., Scarvell, J. M., & Smith, P. N. (2017). Fast and Robust Multi-Modal Image Registration for 3D Knee Kinematics. In DICTA 2017 - 2017 International Conference on Digital Image Computing: Techniques and Applications (Vol. 2017-December, pp. 1–5). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/DICTA.2017.8227434

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 3

43%

Professor / Associate Prof. 2

29%

Researcher 2

29%

Readers' Discipline

Tooltip

Medicine and Dentistry 3

38%

Engineering 3

38%

Philosophy 1

13%

Nursing and Health Professions 1

13%

Save time finding and organizing research with Mendeley

Sign up for free