Next Article in Journal
Railway Crossing Risk Area Detection Using Linear Regression and Terrain Drop Compensation Techniques
Previous Article in Journal
A Framework for a Context-Aware Elderly Entertainment Support System
Sensors 2014, 14(6), 10562-10577; doi:10.3390/s140610562
Article

Diffusion Maps for Multimodal Registration

Received: 14 May 2014; in revised form: 6 June 2014 / Accepted: 6 June 2014 / Published: 16 June 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [412 KB, uploaded 21 June 2014]   |   Browse Figures
Abstract: Multimodal image registration is a difficult task, due to the significant intensity variations between the images. A common approach is to use sophisticated similarity measures, such as mutual information, that are robust to those intensity variations. However, these similarity measures are computationally expensive and, moreover, often fail to capture the geometry and the associated dynamics linked with the images. Another approach is the transformation of the images into a common space where modalities can be directly compared. Within this approach, we propose to register multimodal images by using diffusion maps to describe the geometric and spectral properties of the data. Through diffusion maps, the multimodal data is transformed into a new set of canonical coordinates that reflect its geometry uniformly across modalities, so that meaningful correspondences can be established between them. Images in this new representation can then be registered using a simple Euclidean distance as a similarity measure. Registration accuracy was evaluated on both real and simulated brain images with known ground-truth for both rigid and non-rigid registration. Results showed that the proposed approach achieved higher accuracy than the conventional approach using mutual information.
Keywords: diffusion maps; spectral geometry; diffusion distance; multimodal registration diffusion maps; spectral geometry; diffusion distance; multimodal registration
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Export to BibTeX |
EndNote


MDPI and ACS Style

Piella, G. Diffusion Maps for Multimodal Registration. Sensors 2014, 14, 10562-10577.

AMA Style

Piella G. Diffusion Maps for Multimodal Registration. Sensors. 2014; 14(6):10562-10577.

Chicago/Turabian Style

Piella, Gemma. 2014. "Diffusion Maps for Multimodal Registration." Sensors 14, no. 6: 10562-10577.


Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert