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JACIII Vol.21 No.7 pp. 1280-1290
doi: 10.20965/jaciii.2017.p1280
(2017)

Paper:

Retinal Blood Vessel Segmentation Using Extreme Learning Machine

Fan Guo, Da Xiang, Beiji Zou, Chengzhang Zhu, and Shengnan Wang

School of Information Science and Engineering, Central South University
Changsha, Hunan 410083, China

Corresponding author

Received:
March 19, 2017
Accepted:
September 4, 2017
Published:
November 20, 2017
Keywords:
retinal image, vessel segmentation, Extreme Learning Machine (ELM), Gabor filter, Hessian matrix, bottom-hat transformation
Abstract

Extreme learning machine (ELM) is an effective machine learning technique that widely used in image processing. In this paper, a new supervised method for segmenting blood vessels in retinal images is proposed based on the ELM classifier. The proposed algorithm first constructs a 7-D feature vector using multi-scale Gabor filter, Hessian matrix and bottom-hat transformation. Then, an ELM classifier is trained on gold standard examples of vessel segmentation images to classify previous unseen images. The algorithm was tested on the publicly available DRIVE database – a digital image database for vessel extraction. Experimental results on both real-captured images and public database images demonstrate that our method shows comparative performance against other methods, which make the proposed algorithm a suitable tool for automated retinal image analysis.

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Last updated on Dec. 12, 2017