生物医学工程学杂志

生物医学工程学杂志

基于视觉显著性和旋转扫描的视盘分割新方法

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视盘的快速定位与边缘分割是计算机辅助诊断的重要研究课题。本研究提出了一种有效的视盘分割新方法,将人眼视觉特性引入眼底图像的分析与处理。本文提出的这一方法充分考虑视盘在眼底图像中的形态特征,通过快速定位感兴趣区域,同时融合视盘的亮度、颜色和空间分布等视觉显著性特征,生成了基于像素距离的显著性图,并应用自适应阈值分割视盘。在此基础上,进一步提出旋转扫描方法,以减少血管对视盘完整性的影响和干扰,最终获得连续完整的边缘轮廓。然后,本课题组在眼底图像数据库 Drishti-GS 中验证提出的视盘边缘分割方法是否有效。本文研究结果显示,该方法简单快捷,具有良好的性能指标,能达到眼科专家的分割水平,今后或有助于眼科疾病的计算机辅助诊断。

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.

关键词: 视盘分割; 视觉显著性; 旋转扫描

Key words: optic disk segmentation; visual saliency; rotary scanning

引用本文: 曹新容, 薛岚燕, 林嘉雯, 余轮. 基于视觉显著性和旋转扫描的视盘分割新方法. 生物医学工程学杂志, 2018, 35(2): 229-236. doi: 10.7507/1001-5515.201706013 复制

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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.