Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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Superparsing: scalable nonparametric image parsing with superpixels
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ACM Transactions on Intelligent Systems and Technology (TIST)
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Optic disc segmentation in retinal fundus images is important in computer aided diagnosis. In this paper, an optic disc segmentation method based on superpixel classification is proposed. In the classification, histograms from contrast enhanced image channels and center surround statistics from center surround difference maps are proposed as features to determine each superpixel as disc or non disc. In the training step, bootstrapping is adopted to handle the unbalanced cluster issue due to the presence of peripapillary atrophy. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. The proposed method has been tested on a database of 650 images with optic disc boundaries marked by trained professionals manually. The experimental results show a mean overlapping error of 9.5%, better than previous methods. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The method can be used in computer aided diagnosis systems and the self-assessment can be used as an indicator of results with large errors and thus enhance the clinical deployment of the automatic segmentation.