Publication: Hard exudates segmentation based on learned initial seeds and iterative graph cut
Issued Date
2018-05-01
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ISSN
18727565
01692607
01692607
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2-s2.0-85042421420
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Mahidol University
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SCOPUS
Bibliographic Citation
Computer Methods and Programs in Biomedicine. Vol.158, (2018), 173-183
Suggested Citation
Worapan Kusakunniran, Qiang Wu, Panrasee Ritthipravat, Jian Zhang Hard exudates segmentation based on learned initial seeds and iterative graph cut. Computer Methods and Programs in Biomedicine. Vol.158, (2018), 173-183. doi:10.1016/j.cmpb.2018.02.011 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/45637
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Title
Hard exudates segmentation based on learned initial seeds and iterative graph cut
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Abstract
© 2018 Elsevier B.V. (Background and Objective): The occurrence of hard exudates is one of the early signs of diabetic retinopathy which is one of the leading causes of the blindness. Many patients with diabetic retinopathy lose their vision because of the late detection of the disease. Thus, this paper is to propose a novel method of hard exudates segmentation in retinal images in an automatic way. (Methods): The existing methods are based on either supervised or unsupervised learning techniques. In addition, the learned segmentation models may often cause miss-detection and/or fault-detection of hard exudates, due to the lack of rich characteristics, the intra-variations, and the similarity with other components in the retinal image. Thus, in this paper, the supervised learning based on the multilayer perceptron (MLP) is only used to identify initial seeds with high confidences to be hard exudates. Then, the segmentation is finalized by unsupervised learning based on the iterative graph cut (GC) using clusters of initial seeds. Also, in order to reduce color intra-variations of hard exudates in different retinal images, the color transfer (CT) is applied to normalize their color information, in the pre-processing step. (Results): The experiments and comparisons with the other existing methods are based on the two well-known datasets, e_ophtha EX and DIARETDB1. It can be seen that the proposed method outperforms the other existing methods in the literature, with the sensitivity in the pixel-level of 0.891 for the DIARETDB1 dataset and 0.564 for the e_ophtha EX dataset. The cross datasets validation where the training process is performed on one dataset and the testing process is performed on another dataset is also evaluated in this paper, in order to illustrate the robustness of the proposed method. (Conclusions): This newly proposed method integrates the supervised learning and unsupervised learning based techniques. It achieves the improved performance, when compared with the existing methods in the literature. The robustness of the proposed method for the scenario of cross datasets could enhance its practical usage. That is, the trained model could be more practical for unseen data in the real-world situation, especially when the capturing environments of training and testing images are not the same.