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Detection of hypertensive retinopathy using vessel measurements and textural features

Carla Agurto, Vinayak Joshi, Sheila Nemeth

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 9, 2015
    PubMed

    Insights

    A new system automatically detects hypertensive retinopathy (HR) from retinal images. This AI approach analyzes vessel features, achieving 84% accuracy in classifying HR, aiding early diagnosis.

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Hypertensive retinopathy (HR) features are documented in medical literature.
    • Early detection of HR is crucial for managing hypertension-related complications.
    • Automated systems can aid in the objective assessment of retinal changes.

    Purpose of the Study:

    • To develop and validate an automated system for classifying hypertensive retinopathy (HR) using digital color fundus images.
    • To assess the system's performance in distinguishing between HR and non-HR cases.

    Main Methods:

    • Image normalization, enhancement, and optic disc localization.
    • Retinal vasculature segmentation, measurement of vessel width and tortuosity.
    • Extraction of color, artery-vein ratio, and amplitude-modulation frequency-modulation (AM-FM) features.
    • Classification using linear regression on extracted features.

    Main Results:

    • The system was tested on 74 digital fundus photographs.
    • Leave-one-out cross-validation was employed for performance evaluation.
    • Achieved an area under the ROC curve (AUC) of 0.84.
    • Demonstrated sensitivity of 90% and specificity of 67%.

    Conclusions:

    • The developed automated system shows promise for classifying hypertensive retinopathy.
    • The method utilizes a comprehensive set of image features for HR detection.
    • Further validation on larger datasets may enhance diagnostic capabilities.

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