Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12216/200
Title: An unsupervised learning approach based on Hopfield-like network for assessing Posterior Capsule Opacification
Authors: Werghi, N. 
Sammouda, R. 
Al Kirbi, F. 
Issue Date: 2007
Journal: Proceedings of IAPR Conference on Machine Vision Applications, MVA 2007 
Conference: 10th IAPR Conference on Machine Vision Applications, MVA 2007; Tokyo; Japan; 16 May 2007 through 18 May 2007; Code 95065 
Abstract: Posterior Capsule Opacification (PCO) is the commonest complication of cataract surgery occurring in up to 50% of patients by 2 to 3 years after the operation [1]. This paper proposes a new approach for the assessment of PCO digital images. The approach deploys an unsupervised learning technique for clustering image pixels into different regions based on chromatic attributes. The innovation aspect of this paper, is proposing the number of regions in a clustered image as measurement tool for assessing the PCO. The approach exhibits robustness and stability that would contribute in providing a systematic and objective assessment.
URI: http://hdl.handle.net/20.500.12216/200
DOI: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872542873&partnerID=40&md5=06eaa0bdcb2337aeffbcb5bcfeb85162
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