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Automated registration of multimodal optic disc images: clinical assessment of alignment accuracy

Ng, Wai Siene, Legg, Phil, Avadhanam, Venkat, Aye, Kyaw, Evans, Steffan H. P., North, Rachel Valerie, Marshall, Andrew D., Rosin, Paul L. and Morgan, James Edwards 2016. Automated registration of multimodal optic disc images: clinical assessment of alignment accuracy. Journal of Glaucoma 25 (4) , pp. 397-402. 10.1097/IJG.0000000000000252

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Abstract

Purpose: To determine the accuracy of automated alignment algorithms for the registration of optic disc images obtained by 2 different modalities: fundus photography and scanning laser tomography. Materials and Methods: Images obtained with the Heidelberg Retina Tomograph II and paired photographic optic disc images of 135 eyes were analyzed. Three state-of-the-art automated registration techniques Regional Mutual Information, rigid Feature Neighbourhood Mutual Information (FNMI), and nonrigid FNMI (NRFNMI) were used to align these image pairs. Alignment of each composite picture was assessed on a 5-point grading scale: “Fail” (no alignment of vessels with no vessel contact), “Weak” (vessels have slight contact), “Good” (vessels with <50% contact), “Very Good” (vessels with >50% contact), and “Excellent” (complete alignment). Custom software generated an image mosaic in which the modalities were interleaved as a series of alternate 5×5-pixel blocks. These were graded independently by 3 clinically experienced observers. Results: A total of 810 image pairs were assessed. All 3 registration techniques achieved a score of “Good” or better in >95% of the image sets. NRFNMI had the highest percentage of “Excellent” (mean: 99.6%; range, 95.2% to 99.6%), followed by Regional Mutual Information (mean: 81.6%; range, 86.3% to 78.5%) and FNMI (mean: 73.1%; range, 85.2% to 54.4%). Conclusions: Automated registration of optic disc images by different modalities is a feasible option for clinical application. All 3 methods provided useful levels of alignment, but the NRFNMI technique consistently outperformed the others and is recommended as a practical approach to the automated registration of multimodal disc images.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Mathematics
Optometry and Vision Sciences
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RE Ophthalmology
Publisher: Lippincott Williams & Wilkins
ISSN: 1057-0829
Date of First Compliant Deposit: 21 April 2016
Date of Acceptance: 15 February 2015
Last Modified: 09 May 2019 13:39
URI: http://orca.cf.ac.uk/id/eprint/89580

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