Automatic nuclear cataract grading using image gradients

Ruchir Srivastava, Xinting Gao, Fengshou Yin, Damon W.K. Wong, Jiang Liu, Carol Y. Cheung, Tien Yin Wong

Research output: Journal PublicationArticlepeer-review

17 Citations (Scopus)

Abstract

This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.

Original languageEnglish
Article number014502
JournalJournal of Medical Imaging
Volume1
Issue number1
DOIs
Publication statusPublished - 1 Apr 2014
Externally publishedYes

Keywords

  • automatic grading
  • biomedical image processing
  • image gradients
  • nuclear cataract

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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