Automated glaucoma screening method based on image segmentation and feature extraction
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ORIGINAL ARTICLE
Automated glaucoma screening method based on image segmentation and feature extraction Fan Guo 1
&
Weiqing Li 1 & Jin Tang 1 & Beiji Zou 2 & Zhun Fan 3
Received: 17 February 2020 / Accepted: 25 July 2020 # International Federation for Medical and Biological Engineering 2020
Abstract Glaucoma is a chronic disease that threatens eye health and can cause permanent blindness. Since there is no cure for glaucoma, early screening and detection are crucial for the prevention of glaucoma. Therefore, a novel method for automatic glaucoma screening that combines clinical measurement features with image-based features is proposed in this paper. To accurately extract clinical measurement features, an improved UNet++ neural network is proposed to segment the optic disc and optic cup based on region of interest (ROI) simultaneously. Some important clinical measurement features, such as optic cup to disc ratio, are extracted from the segmentation results. Then, the increasing field of view (IFOV) feature model is proposed to fully extract texture features, statistical features, and other hidden image-based features. Next, we select the best feature combination from all the features and use the adaptive synthetic sampling approach to alleviate the uneven distribution of training data. Finally, a gradient boosting decision tree (GBDT) classifier for glaucoma screening is trained. Experimental results based on the ORIGA dataset show that the proposed algorithm achieves excellent glaucoma screening performance with sensitivity of 0.894, accuracy of 0.843, and AUC of 0.901, which is superior to other existing methods. Keywords Glaucoma screening . Neural network . Image segmentation . Feature extraction
1 Introduction Glaucoma is a common chronic disease that threatens eye health, and it is the second leading cause of blindness worldwide [1]. According to the World Health Organization (WHO), approximately 65 million people around the globe are suffering from glaucoma [2]. Since the vision loss caused by glaucoma is irreversible and the symptoms are imperceptible in the early stages, glaucoma is considered to be “silent theft of sight” [3]. Although existing medical technology cannot cure glaucoma, early screening and corresponding treatment can help patients avoid vision loss and reduce the probability of blindness effectively. * Jin Tang [email protected] 1
School of Automation, Central South University, Changsha 410083, China
2
School of Computer Science and Engineering, Central South University, Changsha 410083, China
3
Key Laboratory of Digital Signal and Image Processing of Guangdong Province, Shantou University, Shantou 515063, China
One common clinical glaucoma detection technique is intraocular pressure (IOP) measurement. Increasing IOP is one of the symptoms of glaucoma; it can lead to optic nerve damage, visual field defects, and even blindness [4]. Therefore, IOP is considered as one of the important indicators of glaucoma. However, this method is inadequate because the IOP of some patients with g
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