An unsupervised learning approach based on a Hopfield-like network for assessing posterior capsule opacification

Naoufel Werghi, Rachid Sammouda, Fatma AlKirbi

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Posterior capsule opacification (PCO) is the most common complication of cataract surgery, occurring in up to 50% of patients by 2-3 years after the operation [Spalton in Eye 13(Pt 3b):489-492, 1999]. 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 innovative aspect of this paper lies in proposing the number of regions in a clustered image as a measurement tool for assessing the PCO. Experiments using synthetic data confirmed the plausibility of this approach. A series of experiments conducted on real PCO images demonstrated the robustness and stability of the proposed algorithm. Finally, the comparison of our method's assessment with medical expert evaluation reveals a very reasonable concordance.

Original languageBritish English
Pages (from-to)383-396
Number of pages14
JournalPattern Analysis and Applications
Volume13
Issue number4
DOIs
StatePublished - 2010

Keywords

  • Hopfield neural network
  • Image clustering
  • Medical images
  • Posterior capsule opacification
  • Unsupervised classification

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