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Multi-parametric optic disc segmentation using superpixel based feature classification
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DOI：10.1016/j.eswa.2018.12.008.png)
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Abstract
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Glaucoma along with diabetic retinopathy is a major cause of vision blindness and is projected to affect over 80 million people by 2020. Recently, expert systems have matched human performance in disease diagnosis and proven to be highly useful in assisting medical experts in the diagnosis and detection of diseases. Hence, automated optic disc detection through intelligent systems is vital for early diagnosis and detection of Glaucoma. This paper presents a multi-parametric optic disk detection and localization method for retinal fundus images using region-based statistical and textural features. Highly discriminative features are selected based on the mutual information criterion and a comparative analysis of four benchmark classifiers: Support Vector Machine, Random Forest (RF), AdaBoost and RusBoost is presented. The results of the proposed RF classifier based pipeline demonstrate its highly competitive performance (accuracies of 0.993, 0.988 and 0.993 on the DRIONS, MESSIDOR and ONHSD databases) with the stateof-the-art, thus making it a suitable candidate for patient management systems for early diagnosis of the Glaucoma. (C) 2018 Elsevier Ltd. All rights reserved.
Keywords:
AdaBoostM1
Glaucoma
RusBoost
Random forest
Support vector machine
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