Copy the page URI to the clipboard
Reel, P.S.; Dooley, L.S.; Wong, K.P. and Börner, A.
(2014).
DOI: https://doi.org/10.1109/VCIP.2014.7051515
Abstract
Images having either the same or different modalities can be aligned using the systematic process of image registration. Inherent image characteristics including intensity non-uniformities in magnetic resonance images and large homogeneous non-vascular regions in retinal and other generic image types however, pose a significant challenge to their registration. This paper presents an adaptive expectation maximisation for principal component analysis with mutual information (aEMPCA-MI) similarity measure for image registration. It introduces a novel iterative process to adaptively select the most significant principal components using Kaiser rule and applies 4-pixel connectivity for feature extraction together with Wichard's bin size selection in calculating the MI. Both quantitative and qualitative results on a diverse range of image datasets, conclusively demonstrate the superior image registration performance of aEMPCA-MI compared with existing MI-based similarity measures.
Viewing alternatives
Download history
Metrics
Public Attention
Altmetrics from AltmetricNumber of Citations
Citations from DimensionsItem Actions
Export
About
- Item ORO ID
- 41015
- Item Type
- Conference or Workshop Item
- ISBN
- 1-4799-6139-6, 978-1-4799-6139-9
- Keywords
- principal component analysis
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Research Group
- Centre for Research in Computing (CRC)
- Copyright Holders
- © 2014 IEEE
- Related URLs
- Depositing User
- Parminder Reel