基于改进图割的超声瞳孔图像分割与直径测量

doi:10.3969/j.issn.0258-8021. 2015. 04.003

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摘要传统的瞳孔直径测量是通过医生手工标定，对于眼外伤和丧失意识的患者测量不方便。针对瞳孔直径测量的人工交互量大且测量鲁棒性不强的问题，采用图割算法分割瞳孔超声图像并测量瞳孔直径。对传统图割算法进行两个方面的改进，采用自适应阈值的区域生长代替人为种子点选取，在保证分割效果的基础上减少了图割的交互量；在能量函数的数据项部分增加图像的梯度信息，减少了原始算法分割结果中出现的小区域，增强了对弱边缘的分割。最后，对采集到的超声瞳孔图像进行自动分割、自动测量瞳孔直径，可以得到患者瞳孔的直径动态变化，给临床诊断提供依据。为了验证算法的有效性，对10位患者的动态瞳孔超声图像进行基于改进图割的瞳孔直径测量，并与医生的手动测量结果对比。结果表明，本方法的结果与医生手动测量结果的绝对误差小于0.2 mm，相关系数不小于0.83。通过改进图割算法，改善了分割效果，实现了超声瞳孔动态图像的自动直径测量，并可有效代替瞳孔直径的人工测量，减少人工交互量。

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	周亚丽¹高玉芝²周浩¹周密¹郑音飞^1*

关键词 ：图割, 超声瞳孔图像, 图像分割, 瞳孔直径

Abstract：The traditional measurement of pupil diameter is manually measured, but for patients who have ocular trauma or loss of consciousness this method is inconvenience. Aiming to solve the problem of large manual interaction in pupil diameter measurement and the weak measuring robustness, we used improved graph cut segmentation algorithm to segment pupil ultrasound images and measure pupil diameters. In this paper, we improved the traditional graph cut algorithm in two aspects. One is using adaptive threshold region growing to take place of manually seeds selection, which ensures the segmentation results while reducing the amount of manual interaction. The other one is increasing gradient information of the image into data entry portion of the energy function, which reduces the small area in the segmentation results and enhances the weak edges of segmentation. The proposed method realized automatic segmentation of image and automatic measurement of the pupil diameter. By employing the method we acquired dynamic changes of patient pupil diameter and provide a basis for clinical diagnosis. To verify the validity of the algorithm, we used the method to measure the diameter of ten patients’ dynamic pupil ultrasound images, and compared the results with that obtained by manual measurements. It is shown that the absolute error is less than 0.2 mm, the correlation coefficient is at least 0.83. In conclusion, the modified graphcut algorithm improved segmentation results, achieved automatical measurements of pupil diameters using dynamic ultrasound pupil images, and can be expected to substitute manual measurements and reduce the amount of human interaction.

Key words： graphcut pupil ultrasound image image segmentation pupil diameter

基金资助:中央高校基本科研业务费专项资金（2015FZA5019）；国家“十二五”科技支撑计划资助项目（2011BAI12B02）

引用本文:

周亚丽¹高玉芝²周浩¹周密¹郑音飞^1*. 基于改进图割的超声瞳孔图像分割与直径测量[J]. 中国生物医学工程学报, 2015, 34(4): 399-406.
Zhou Yali¹Gao Yuzhi²Zhou Hao¹Zhou Mi¹Zheng Y. Pupil Ultrasound Images Segmentation and Diameter Measurements Based on Improved Graph Cuts. journal1, 2015, 34(4): 399-406.

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［1］Wingate M, Hood B, Shi H, et al. Automated pupil size determination for evaluating fluctuations in physiological arousal ［C］ //Yue Minzhu, Feng Yang, eds. Signal Processing Proceedings. Beijing: IEEE, 1998:1666-1669.
［2］Parnandi A, GutierrezOsuna R. Contactless measurement of heart rate variability from pupillary fluctuations ［C］ //Suter G, eds. Affective Computing and Intelligent Interaction. Geneva: IEEE, 2013:191-196.
［3］Yoshida H, Mizuta H, Gouhara T, et al. Statistical properties of simultaneously recorded fluctuations in pupil diameter and heart rate ［C］ //Hood L, eds. Engineering in Medicine and Biology Society. Montreal : IEEE, 1995: 165-166.
［4］Yamaji K, Hiratai Y, Usui S. The pupil as a possible monitor as the autonomic nervous system ［C］ //Hood L, eds. Engineering in Medicine and Biology Society. Chicago: IEEE, 1997: 2777-2781.
［5］汪育文，保金华，吕帆. 三种瞳孔直径测量方法在不同测量状态下测量结果的比较和分析［J］. 眼视光学杂志, 2009, 11(3): 218-220.
［6］Starck T, Liu Yun, Prewett AL, et al. Comparison of scotopic pupil measurement with slitlampbased cobalt blue light and infrared videobased system ［J］. Journal of Cataract and Refractive Surgery, 2002, 28(11): 1952-1956. 
［7］杨亚波, 俞一波, 付清. Ⅱ型糖尿病患者瞳孔相关性自主神经病变的研究［J］. 中华眼科杂志, 2006, 42(7): 616-619. 
［8］Yang H, Lee M, Kim JB, et al. Burstshot infrared digital photography to determine scotopic pupil diameter ［J］. Journal of Cataract and Refractive Surgery, 2006, 32(12): 2113-2117. 
［9］Schnitzler EM, Baumeister M, Kohnen T. Scotopic measurement of normal pupils: colvard versus video vision analyzer infrared pupillometer ［J］. Journal of Cataract and Refractive Surgery, 2000, 26(6): 859-866. 
［10］Arthur C, Dennis L. Comparison of the colvard pupillometer and the zywave for measuring scotopic pupil diameter ［J］. Journal of Refractive Surgery, 2004, 20(3): 248-252. 
［11］Fotiou F, Fountoulakis KN, Goulas A, et al. Automated standardized pupillometry with optical method for purposes of clinical practice and research ［J］. Clinical Physiology, 2000, 20(5): 336-347. 
［12］Pascal SCJ, Rosen P. Ocular examination technique for the emergency department ［J］. The Journal of Emergency Medicine, 1997, 15(6): 793-810.
［13］Harries A, Shah S, Teismann N, et al. Ultrasound assessment of extraocular movements and pupillary light reflex in ocular trauma ［J］. American Journal of Emergency Medicine, 2010, 28(8): 956-959.
［14］Terrence D. Ultrasound physics and instrumentation ［J］. Surgical Clinics of North America, 1998, 78(2): 197-217.
［15］汪一休. 关于交互式图像分割算法的研究［D］. 合肥: 中国科学技术大学, 2009.
［16］Chang Huali, Chen Zhenping, Huang Qinghua, et al. Graphbased learning for segmentation of 3D ultrasound images ［J］. Neurocomputing, 2015, 151(2): 632-644.
［17］Malcolm J, Rathi Y, Tannenbaum A. Graph cut segmentation with nonlinear shape priors ［C］ //Mahesh P, eds. Image Processing. San Antonio: IEEE, 2007: 365-368.
［18］Slabaugh G, Unal G. Graph cuts segmentation using an elliptical shape prior ［C］ //Mahesh P, eds. Image Processing. San Antonio: IEEE, 2005: 365-368.
［19］Veksler O. Star shape prior for graphcut image segmentation ［C］ //David F, Philip T, Andrew Z, eds. Computer Vision. Marseille: Springer Berlin Heidelberg, 2008:454-467.［20］胡焦, 陈辉. 基于像素相似性与GraphCut 的图像自动分割［J］. 现代电子技术, 2004, 37(2): 45-47.
［21］徐昱琳, 张雷, 陈万米,等. 基于SIFT 特征和图割算法的图像分割方法研究［J］. 计算机测量与控制, 2009, 17(12): 2491-2493.
［22］Kolmogorov V, Zabih R. What energy functions can be minimized via graph cuts? ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligenc, 2004, 26(2): 147-159.
［23］Boykov Y, Veksler O, Zabih R. Fast approxamate energy minimization via graph cuts ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(11): 1222-1239.
［24］Boykov Y, Kolmogorov V. An experimental comparison of mincut/maxflow algorithms for energy minimization in vision ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9): 1124-1137.