Retinal vessel segmentation in colour fundus images using Extreme Learning Machine.
Attributes of the retinal vessel play important role in systemic conditions and ophthalmic diagnosis. In this paper, a supervised method based on Extreme Learning Machine (ELM) is proposed to segment retinal vessel. Firstly, a set of 39-D discriminative feature vectors, consisting of local features, morphological features, phase congruency, Hessian and divergence of vector fields, is extracted for each pixel of the fundus image. Then a matrix is constructed for pixel of the training set based on the feature vector and the manual labels, and acts as the input of the ELM classifier. The output of classifier is the binary retinal vascular segmentation. Finally, an optimization processing is implemented to remove the region less than 30 pixels which is isolated from the retinal vascilar. The experimental results testing on the public Digital Retinal Images for Vessel Extraction (DRIVE) database demonstrate that the proposed method is much faster than the other methods in segmenting the retinal vessels. Meanwhile the average accuracy, sensitivity, and specificity are 0.9607, 0.7140 and 0.9868, respectively. Moreover the proposed method exhibits high speed and robustness on a new Retinal Images for Screening (RIS) database. Therefore it has potential applications for real-time computer-aided diagnosis and disease screening.
School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
Chengzhang
C
Zhu
School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
Beiji
B
Zou
School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
Rongchang
R
Zhao
School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
Jinkai
J
Cui
Ophthalmology Department of the Second Xiangya Hospital, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
Xuanchu
X
Duan
School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China.
Zailiang
Z
Chen
School of Information Science and Engineering, Central South University, Changsha, China; Mobile Health Ministry of Education-China Mobile Joint Laboratory, Changsha, China. Electronic address: yxliang@csu.edu.cn.
Yixiong
Y
Liang
D002681:China / Q000208:ethnology; D003116:Color; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D005654:Fundus Oculi; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D012171:Retinal Vessels / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D000069553:Supervised Machine Learning
Colour fundus image; Computer-aided diagnosis; Feature extraction; Retinal vessel segmentation; Supervised learning