Brain-inspired algorithms for retinal image analysis

作者：Romeny, Bart M. ter Haar^*; Bekkers, Erik J.; Zhang, Jiong; Abbasi-Sureshjani, Samaneh; Huang, Fan; Duits, Remco; Dashtbozorg, Behdad; Berendschot, Tos T. J. M.; Smit-Ockeloen, Iris; Eppenhof, Koen A. J.; Feng, Jinghan; Hannink, Julius; Schouten, Jan; Tong, Mengmeng; Wu, Hanhui; van Triest, Han W.; Zhu, Shanshan; Chen, Dali; He, Wei; Xu, Ling; Han, Ping; Kang, Yan

来源：Machine Vision and Applications, 2016, 27(8): 1117-1135.

DOI：10.1007/s00138-016-0771-9

• Retina
• Diabetic retinopathy
• Multi-orientation
• Orientation scores
• Vessel analysis
• Tortuosity
• SE(2)
• Screening

Retinal image analysis is a challenging problem due to the precise quantification required and the huge numbers of images produced in screening programs. This paper describes a series of innovative brain-inspired algorithms for automated retinal image analysis, recently developed for the RetinaCheck project, a large-scale screening program for diabetic retinopathy and other retinal diseases in Northeast China. The paper discusses the theory of orientation scores, inspired by cortical multi-orientation pinwheel structures, and presents applications for automated quality assessment, optic nerve head detection, crossing-preserving enhancement and segmentation of retinal vasculature, arterio-venous ratio, fractal dimension, and vessel tortuosity and bifurcations. Many of these algorithms outperform state-of-the-art techniques. The methods are currently validated in collaborating hospitals, with a rich accompanying base of metadata, to phenotype and validate the quantitative algorithms for optimal classification power.

• 出版日期2016-11
• 单位中国医科大学; 东北大学