A novel method of optic disk segmentation based on visual saliency and rotary scanning_Journal of Biomedical Engineering

Authors：

 CAO Xinrong ^1,2 , XUE Lanyan ¹ , LIN Jiawen ¹ , YU Lun ¹

1. College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116, P.R.China;
2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou 350121, P.R.China;

Corresponding author：

CAO Xinrong, Email: cxrxmu@163.com

Keywords：

optic disk segmentation; visual saliency; rotary scanning

DOI：

10.7507/1001-5515.201706013

Video：

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Fast optic disk localization and boundary segmentation is an important research topic in computer aided diagnosis. This paper proposes a novel method to effectively segment optic disk by using human visual characteristics in analyzing and processing fundus image. After a general analysis of optic disk features in fundus images, the target of interest could be located quickly, and intensity, color and spatial distribution of the disc are used to generate saliency map based on pixel distance. Then the adaptive threshold is used to segment optic disk. Moreover, to reduce the influence of vascular, a rotary scanning method is devised to achieve complete and continuous contour of optic disk boundary. Tests in the public fundus images database Drishti-GS have good performances, which mean that the proposed method is simple and rapid, and it meets the standard of the eye specialists. It is hoped that the method could be conducive to the computer aided diagnosis of eye diseases in the future.

Citation： CAO Xinrong, XUE Lanyan, LIN Jiawen, YU Lun. A novel method of optic disk segmentation based on visual saliency and rotary scanning. Journal of Biomedical Engineering, 2018, 35(2): 229-236. doi: 10.7507/1001-5515.201706013 Copy

1.	邹北骥, 张思剑, 朱承璋. 彩色眼底图像视盘自动定位与分割. 光学精密工程, 2015, 23(4): 1187-1195.
2.	Aquino A. Gegúndez-Arias M E, Marín D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Transactions on Medical Imaging, 2010, 29(11): 1860-1869.
3.	Besenczi R, Toth J, Hajdu A. A review on automatic analysis techniques for color fundus photographs. Comput Struct Biotechnol J, 2016, 14: 371-384.
4.	Lu Shijian, Lim J H. Automatic optic disc detection from retinal images by a line operator. IEEE Trans Biomed Eng, 2011, 58(1): 88-94.
5.	Lu Shijian. Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans Med Imaging, 2011, 30(12): 2126-2133.
6.	Yu H, Barriga E S, Agurto C, et al. Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level Sets. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(4): 644-657.
7.	Youssif A R, Ghalwash A Z, Ghoneim A R. Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter. IEEE Transactions on Medical Imaging, 2008, 27(1): 11-18.
8.	郑绍华, 陈健, 潘林, 等. 基于定向局部对比度的眼底图像视盘检测方法. 中国生物医学工程学报, 2014, 33(3): 289-296.
9.	Garduno-Alvarado T, Elena Martinez-Perez M, Ana Martinez-Castellanos M, et al. Fast optic disc segmentation in fundus images//proceedings of 2016 future technologies conference (FTC), San Francisco, 2016: 1335-1339.
10.	Saleh M D, Salih N D, Eswaran C, et al. Automated segmentation of optic disc in fundus images//2014 IEEE 10th international colloquium on signal processing and its applications (CSPA 2014), Kuala Lumpur, 2014: 145-150.
11.	张东波, 易瑶, 赵圆圆. 基于投影的视网膜眼底图像视盘检测方法. 中国生物医学工程学报, 2013, 32(4): 477-483.
12.	Cheng Jun, Liu Jiang, Xu Yanwu, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging, 2013, 32(6): 1019-1032.
13.	Mahapatra D, Buhmann J M. A field of experts model for optic cup and disc segmentation from retinal fundus images//2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Brooklyn, 2015: 218-221.
14.	Mittapalli P S, Kande G B. Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomed Signal Process Control, 2016, 24: 34-46.
15.	Itti L, Koch C. Computational modeling of visual attention. Nat Rev Neurosci, 2001, 2(3): 194-203.
16.	柯鑫, 江威, 朱江兵. 基于视觉注意机制的眼底图像视盘快速定位与分割. 科学技术与工程, 2015, 15(35): 47-53.
17.	裴晓敏, 季久玉, 刘文进. 基于视觉显著性特征的乳腺肿块检测方法. 光电子•激光, 2017, 28(1): 117-122.
18.	Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-gs: retinal image dataset for optic nerve head(onh) segmentation// IEEE ISBI, Beijing, 2014: 53-56.
19.	董琳, 赵尔敦, 刘心馨, 等. 一种新型的视盘分割方法. 计算机与数字工程, 2015, 43(7): 1333-1336, 1364.
20.	Zhang Zheng, Han Xiao, Pearson E, et al. Artifact reduction in short-scan CBCT by use of optimization-based reconstruction. Phys Med Biol, 2016, 61(9): 3387-3406.
21.	Zhang Zheng, Ye Jinghan, Chen Buxin, et al. Investigation of optimization-based reconstruction with an image-total-variation constraint in PET. Phys Med Biol, 2016, 61(16): 6055-6084.
22.	Zhang Zheng, Xia Dan, Han Xiao, et al. Impact of image constraints and object structures on Optimization-Based Reconstruction//Proceedings of The 4th International Conference on Image Formation in X-Ray Computed Tomography, Bamberg, 2016: 487-490.

1. 邹北骥, 张思剑, 朱承璋. 彩色眼底图像视盘自动定位与分割. 光学精密工程, 2015, 23(4): 1187-1195.
2. Aquino A. Gegúndez-Arias M E, Marín D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Transactions on Medical Imaging, 2010, 29(11): 1860-1869.
3. Besenczi R, Toth J, Hajdu A. A review on automatic analysis techniques for color fundus photographs. Comput Struct Biotechnol J, 2016, 14: 371-384.
4. Lu Shijian, Lim J H. Automatic optic disc detection from retinal images by a line operator. IEEE Trans Biomed Eng, 2011, 58(1): 88-94.
5. Lu Shijian. Accurate and efficient optic disc detection and segmentation by a circular transformation. IEEE Trans Med Imaging, 2011, 30(12): 2126-2133.
6. Yu H, Barriga E S, Agurto C, et al. Fast localization and segmentation of optic disk in retinal images using directional matched filtering and level Sets. IEEE Transactions on Information Technology in Biomedicine, 2012, 16(4): 644-657.
7. Youssif A R, Ghalwash A Z, Ghoneim A R. Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter. IEEE Transactions on Medical Imaging, 2008, 27(1): 11-18.
8. 郑绍华, 陈健, 潘林, 等. 基于定向局部对比度的眼底图像视盘检测方法. 中国生物医学工程学报, 2014, 33(3): 289-296.
9. Garduno-Alvarado T, Elena Martinez-Perez M, Ana Martinez-Castellanos M, et al. Fast optic disc segmentation in fundus images//proceedings of 2016 future technologies conference (FTC), San Francisco, 2016: 1335-1339.
10. Saleh M D, Salih N D, Eswaran C, et al. Automated segmentation of optic disc in fundus images//2014 IEEE 10th international colloquium on signal processing and its applications (CSPA 2014), Kuala Lumpur, 2014: 145-150.
11. 张东波, 易瑶, 赵圆圆. 基于投影的视网膜眼底图像视盘检测方法. 中国生物医学工程学报, 2013, 32(4): 477-483.
12. Cheng Jun, Liu Jiang, Xu Yanwu, et al. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Trans Med Imaging, 2013, 32(6): 1019-1032.
13. Mahapatra D, Buhmann J M. A field of experts model for optic cup and disc segmentation from retinal fundus images//2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), Brooklyn, 2015: 218-221.
14. Mittapalli P S, Kande G B. Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomed Signal Process Control, 2016, 24: 34-46.
15. Itti L, Koch C. Computational modeling of visual attention. Nat Rev Neurosci, 2001, 2(3): 194-203.
16. 柯鑫, 江威, 朱江兵. 基于视觉注意机制的眼底图像视盘快速定位与分割. 科学技术与工程, 2015, 15(35): 47-53.
17. 裴晓敏, 季久玉, 刘文进. 基于视觉显著性特征的乳腺肿块检测方法. 光电子•激光, 2017, 28(1): 117-122.
18. Sivaswamy J, Krishnadas S R, Joshi G D, et al. Drishti-gs: retinal image dataset for optic nerve head(onh) segmentation// IEEE ISBI, Beijing, 2014: 53-56.
19. 董琳, 赵尔敦, 刘心馨, 等. 一种新型的视盘分割方法. 计算机与数字工程, 2015, 43(7): 1333-1336, 1364.
20. Zhang Zheng, Han Xiao, Pearson E, et al. Artifact reduction in short-scan CBCT by use of optimization-based reconstruction. Phys Med Biol, 2016, 61(9): 3387-3406.
21. Zhang Zheng, Ye Jinghan, Chen Buxin, et al. Investigation of optimization-based reconstruction with an image-total-variation constraint in PET. Phys Med Biol, 2016, 61(16): 6055-6084.
22. Zhang Zheng, Xia Dan, Han Xiao, et al. Impact of image constraints and object structures on Optimization-Based Reconstruction//Proceedings of The 4th International Conference on Image Formation in X-Ray Computed Tomography, Bamberg, 2016: 487-490.

Journal of Biomedical Engineering

A novel method of optic disk segmentation based on visual saliency and rotary scanning

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