Nonlinear retinal image enhancement for vessel detection

Xiaohong Wang; Xudong Jiang

doi:10.1117/12.2281566

21 July 2017 Nonlinear retinal image enhancement for vessel detection

Xiaohong Wang, Xudong Jiang

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 104202M (2017) https://doi.org/10.1117/12.2281566
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

Retinal vessel detection is an essential part of the computer-aided diagnosis of eye diseases. Due to non-perfect imaging environment, retinal images often appear with intensity variations and artificial noises. This work proposes a two-step nonlinear retinal image enhancement to compensate for those imperfections of retinal images. The first step reduces intensity fluctuations of the image and the second step attenuates impulsive noise while preserving retinal vessels. Classification on the feature vector extracted from the enhanced retinal images is performed by using a linear SVM classifier. Experimental results demonstrate that the proposed method of two-step nonlinear image enhancement visibly improves the vessel detection performance, achieving better accuracy than that without enhancement process on the both DRIVE and STARE databases.

Citation Download Citation

Xiaohong Wang and Xudong Jiang "Nonlinear retinal image enhancement for vessel detection", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202M (21 July 2017); https://doi.org/10.1117/12.2281566

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