Robust retinal image registration using expectation maximisation with mutual information

Parminder Singh Reel, Laurence S. Dooley, K. C. P. Wong, Anko Borner

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

Retinal images (RI) are widely used to diagnose a variety of eye conditions and diseases such as myopia and diabetic retinopathy. They are inherently characterised by having nonuniform illumination and low-contrast homogeneous regions which represent a unique set of challenges for retinal image registration (RIR). This paper investigates using the expectation maximization for principal component analysis based mutual information (EMPCA-MI) algorithm in RIR. It combines spatial features with mutual information to efficiently achieve improved registration performance. Experimental results for mono-modal RI datasets verify that EMPCA-MI together with Powell-Brent optimization affords superior robustness in comparison with existing RIR methods, including the geometrical features method.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
Pages1118-1122
Number of pages5
ISBN (Electronic)9781479903566
DOIs
Publication statusPublished - 21 Oct 2013
Event2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Vancouver, Canada
Duration: 26 May 201331 May 2013

Conference

Conference2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
CountryCanada
CityVancouver
Period26/05/1331/05/13

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  • Cite this

    Reel, P. S., Dooley, L. S., Wong, K. C. P., & Borner, A. (2013). Robust retinal image registration using expectation maximisation with mutual information. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 1118-1122). IEEE. https://doi.org/10.1109/ICASSP.2013.6637824