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Journal of Healthcare Engineering
Volume 2017, Article ID 5645498, 16 pages
https://doi.org/10.1155/2017/5645498
Research Article

An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis

1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
2Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Beijing 100730, China

Correspondence should be addressed to Huiqi Li; nc.ude.tib@iliqiuh

Received 24 October 2016; Revised 22 January 2017; Accepted 13 February 2017; Published 26 April 2017

Academic Editor: Fabrice Meriaudeau

Copyright © 2017 Li Xiong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Cataract is one of the leading causes of blindness in the world’s population. A method to evaluate blurriness for cataract diagnosis in retinal images with vitreous opacity is proposed in this paper. Three types of features are extracted, which include pixel number of visible structures, mean contrast between vessels and background, and local standard deviation. To avoid the wrong detection of vitreous opacity as retinal structures, a morphological method is proposed to detect and remove such lesions from retinal visible structure segmentation. Based on the extracted features, a decision tree is trained to classify retinal images into five grades of blurriness. The proposed approach was tested using 1355 clinical retinal images, and the accuracies of two-class classification and five-grade grading compared with that of manual grading are 92.8% and 81.1%, respectively. The kappa value between automatic grading and manual grading is 0.74 in five-grade grading, in which both variance and value are less than 0.001. Experimental results show that the grading difference between automatic grading and manual grading is all within 1 grade, which is much improvement compared with that of other available methods. The proposed grading method provides a universal measure of cataract severity and can facilitate the decision of cataract surgery.