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Proceedings Paper

Retinal image quality assessment using generic features
Author(s): Mahnaz Fasih; J.M. Pierre Langlois; Houssem Ben Tahar; Farida Cheriet
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Paper Abstract

Retinal image quality assessment is an important step in automated eye disease diagnosis. Diagnosis accuracy is highly dependent on the quality of retinal images, because poor image quality might prevent the observation of significant eye features and disease manifestations. A robust algorithm is therefore required in order to evaluate the quality of images in a large database. We developed an algorithm for retinal image quality assessment based on generic features that is independent from segmentation methods. It exploits the local sharpness and texture features by applying the cumulative probability of blur detection metric and run-length encoding algorithm, respectively. The quality features are combined to evaluate the image’s suitability for diagnosis purposes. Based on the recommendations of medical experts and our experience, we compared a global and a local approach. A support vector machine with radial basis functions was used as a nonlinear classifier in order to classify images to gradable and ungradable groups. We applied our methodology to 65 images of size 2592×1944 pixels that had been graded by a medical expert. The expert evaluated 38 images as gradable and 27 as ungradable. The results indicate very good agreement between the proposed algorithm’s predictions and the medical expert’s judgment: the sensitivity and specificity for the local approach are respectively 92% and 94%. The algorithm demonstrates sufficient robustness to identify relevant images for automated diagnosis.

Paper Details

Date Published: 24 March 2014
PDF: 7 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352Z (24 March 2014); doi: 10.1117/12.2043325
Show Author Affiliations
Mahnaz Fasih, Ecole Polytechnique de Montréal (Canada)
J.M. Pierre Langlois, Ecole Polytechnique de Montréal (Canada)
Houssem Ben Tahar, DIAGNOS Inc. (Canada)
Farida Cheriet, Ecole Polytechnique de Montréal (Canada)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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