An unsupervised learning approach based on Hopfield-like network for assessing Posterior Capsule Opacification

Naoufel Werghi, Rachid Sammouda, Fatma Al Kirbi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageBritish English
Title of host publicationProceedings of IAPR Conference on Machine Vision Applications, MVA 2007
Pages416-419
Number of pages4
StatePublished - 2007
Event10th IAPR Conference on Machine Vision Applications, MVA 2007 - Tokyo, Japan
Duration: 16 May 200718 May 2007

Publication series

NameProceedings of IAPR Conference on Machine Vision Applications, MVA 2007

Conference

Conference10th IAPR Conference on Machine Vision Applications, MVA 2007
Country/TerritoryJapan
CityTokyo
Period16/05/0718/05/07

Fingerprint

Dive into the research topics of 'An unsupervised learning approach based on Hopfield-like network for assessing Posterior Capsule Opacification'. Together they form a unique fingerprint.

Cite this