Image based grading of nuclear cataract by SVM regression

Li Huiqi, Hwee Lim Joo, Liu Jiang, Yin Wong Tien, Ava Tan, Jin Wang Jie, Paul Mitchell

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

9 Citations (Scopus)


Cataract is one of the leading causes of blindness worldwide. A computer-aided approach to assess nuclear cataract automatically and objectively is proposed in this paper. An enhanced Active Shape Model (ASM) is investigated to extract robust lens contour from slit-lamp images. The mean intensity in the lens area, the color information on the central posterior subcapsular reflex, and the profile on the visual axis are selected as the features for grading. A Support Vector Machine (SVM) scheme is proposed to grade nuclear cataract automatically. The proposed approach has been tested using the lens images from Singapore National Eye Centre. The mean error between the automatic grading and grader's decimal grading is 0.38. Statistical analysis shows that 97.8% of the automatic grades are within one grade difference to human grader's integer grades. Experimental results indicate that the proposed automatic grading approach is promising in facilitating nuclear cataract diagnosis.

Original languageEnglish
Title of host publicationMedical Imaging 2008 - Computer-Aided Diagnosis
Publication statusPublished - 2008
Externally publishedYes
EventMedical Imaging 2008 - Computer-Aided Diagnosis - San Diego, CA, United States
Duration: 19 Feb 200821 Feb 2008

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2008 - Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego, CA


  • Active shape model
  • Nuclear cataract
  • Slit-lamp image
  • SVM regression

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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