Automatic optic disc detection using low-rank representation based semi-supervised extreme learning machine

  • Wei Zhou
  • Shaojie QiaoEmail author
  • Yugen YiEmail author
  • Nan Han
  • Yuqi Chen
  • Gang Lei
Original Article


Optic disc detection plays an important role in developing automatic screening systems for diabetic retinopathy. Several supervised learning-based approaches have been proposed for optic disc detection. However, these approaches demand that the input training examples are completely labelled. Essentially, in medical image analysis, it is difficult to prepare several training samples which were given reliable class labels due to the fact that manually labelling data is very expensive. Moreover, retinal images such as complex vessels structures in the optic disc constituting nonlinear relationships in high-dimensional observation space, which cannot work well by traditional linear classifiers. In this study, a novel approach named low-rank representation based semi-supervised extreme learning machine (LRR-SSELM) is proposed for automated optic disc detection. Our model has the following advantages. First, it detects the optic disc from the viewpoint of semi-supervised learning and overcomes the problem there are small portion of labelled samples. Second, a nonlinear classifier is introduced into our model to fully explore the nonlinear data. Third, the local and global structures of original data can be greatly persevered by low-rank representation (LRR). The performance of the proposed method is validated on three publicly available databases, DIARETDB0, DIARETDB1 and Messidor. The experimental results indicate the advantages and effectiveness of the proposed approach.


Retinal fundus images Optic disc Low-rank representation Semi-supervised extreme learning machine 



This study was supported in part by the National Natural Science Foundation of China under Grant Nos. 61602221, 61772091, 61762050, 61802035 and 4166108; the Natural Science Foundation of Jiangxi Province under Grant No. 20171BAB212009; the Sichuan Science and Technology Program under Grant No. 2018JY0448; the National Natural Science Foundation of Guangxi under Grant No. 2018GXNSFDA138005; the Innovative Research Team Construction Plan in Universities of Sichuan Province under Grant No. 18TD0027; the Scientific Research Foundation for Advanced Talents of Chengdu University of Information Technology under Grant Nos. KYTZ201715 and KYTZ201750; the Scientific Research Foundation for Young Academic Leaders of Chengdu University of Information Technology under Grant No. J201701; Guangdong Pre-national project under Grant No. 2014GKXM054.

Compliance with ethical standards

Conflict of interest

All authors declare that this support does not lead to any conflict of interest regarding the publication of this paper.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Computer ScienceShenyang Aerospace UniversityShenyangChina
  2. 2.School of Software EngineeringChengdu University of Information TechnologyChengduChina
  3. 3.School of SoftwareJiangxi Normal UniversityNanchangChina
  4. 4.School of ManagementChengdu University of Information TechnologyChengduChina

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