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Updated: May 18, 2026

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Peripapillary atrophy detection by sparse biologically inspired feature manifold.

Jun Cheng1, Dacheng Tao, Jiang Liu

  • 1iMED Ocular Imaging Programme, Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore. jcheng@i2r.a-star.edu.sg

IEEE Transactions on Medical Imaging
|September 19, 2012
PubMed
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This study introduces a novel Biologically Inspired Feature (BIF) method for automatically detecting peripapillary atrophy (PPA), achieving over 90% accuracy. This automated approach significantly aids in diagnosing eye diseases like myopia and glaucoma.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Peripapillary atrophy (PPA) is a key indicator for diagnosing myopia and glaucoma.
  • Manual detection of PPA from retinal images is time-consuming and subjective.
  • Automated detection methods are needed to improve efficiency and accuracy in screening programs.

Purpose of the Study:

  • To develop an automated method for detecting peripapillary atrophy (PPA) using Biologically Inspired Features (BIF).
  • To investigate the effectiveness of sparse transfer learning for PPA detection.
  • To improve the accuracy and efficiency of PPA diagnosis.

Main Methods:

  • Segmentation of focal regions from retinal images.
  • Extraction of Biologically Inspired Features (BIF).

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  • Application of sparse transfer learning with selective pair-wise discriminant analysis (negative and positive strategies).

    • Main Results:

    • The proposed BIF-based method achieved over 90% accuracy in PPA detection.
    • Negative sparse transfer learning outperformed positive sparse transfer learning for this task.
    • The method demonstrated superior performance compared to previous approaches.

    Conclusions:

    • The BIF-based approach offers a highly accurate and efficient method for automated PPA detection.
    • This technology can reduce the workload for ophthalmologists and lower diagnostic costs.
    • Automated PPA detection holds significant potential for large-scale eye disease screening programs.