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收稿：2019-08-20，
修回：2019-12-13，
纸质出版：2020-06-16
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目的
2
青光眼是一种可导致视力严重减弱甚至失明的高发眼部疾病。在眼底图像中，视杯和视盘的检测是青光眼临床诊断的重要步骤之一。然而，眼底图像普遍是灰度不均匀的，眼底结构复杂，不同结构之间的灰度重叠较多，受到血管和病变的干扰较为严重。这些都给视盘与视杯的分割带来很大挑战。因此，为了更准确地提取眼底图像中的视杯和视盘区域，提出一种基于双层水平集描述的眼底图像视杯视盘分割方法。
方法
2
通过水平集函数的不同层级分别表示视杯轮廓和视盘轮廓，依据视杯与视盘间的位置关系建立距离约束，应用图像的局部信息驱动活动轮廓演化，克服图像的灰度不均匀性。根据视杯与视盘的几何形状特征，引入视杯与视盘形状的先验信息约束活动轮廓的演化，从而实现视杯与视盘的准确分割。
结果
2
本文使用印度Aravind眼科医院提供的具有视杯和视盘真实轮廓注释的CDRISHTI-GS1数据集对本文方法进行实验验证。该数据集主要用来验证视杯及视盘分割方法的鲁棒性和有效性。本文方法在数据集上对视杯和视盘区域进行分割，取得了67.52%的视杯平均重叠率，81.04%的视盘平均重叠率，0.719的视杯F1分数和0.845的视盘F1分数，结果优于基于COSFIRE（combination of shifted filter responses）滤波模型的视杯视盘分割方法、基于先验形状约束的多相Chan-Vese（C-V）模型和基于聚类融合的水平集方法。
结论
2
实验结果表明，本文方法能够有效克服眼底图像灰度不均匀、血管及病变区域的干扰等影响，更为准确地提取视杯与视盘区域。
Objective
2
Glaucoma is one of the most common eye diseases it can lead to severe vision loss and even permanent blindness. Glaucoma is the primary cause of irreversible blindness worldwide. Diagnosing the disease in its early stage is significant for decreasing the mental and financial pressures of patients. The diagnosis of glaucoma using retinal fundus images is achieved by calculating cup to disc ratio (CDR). The optic disc (OD) is a central yellowish region in retinal fundus images. The optic cup (OC) is the bright central part of the OD that presents variable sizes. Any changes in the shapes of OD and OC may indicate glaucoma. A high CDR value shows that the eye is suffering from glaucoma to a large extent. Therefore
the detection of OC and OD in fundus images is an important step in the clinical diagnosis of glaucoma. The accurate segmentation of OC and OD is facing serious challenges
including severe intensity inhomogeneity
complex fundus image structure
grayscale overlap between different structures
and interference with blood vessels and lesions. Therefore
to accurately segment the OC and OD regions in fundus images
we present a novel OC and OD segmentation method based on a two-layer level set describer in this study. This approach can simultaneously extract the boundaries of OD and OC.
Method
2
Our proposed method consists of four steps. First
OC and OD contours are represented by two different layers of a one-level set function by constructing a two-layer level set model. Fundus images are typically inhomogeneous in gray levels. OC and OD regions exhibit different brightness characteristics compared with the background region in retinal fundus images. Therefore
the local intensity information of an image is used as data term to derive the evolution of the active contours and overcome intensity inhomogeneity in fundus images. Second
the distance constraint is established on the basis of the position relationship between OC and OD. The contours of OC and OD in fundus images are approximately two ellipse shapes. OC is located inside OD
and the distance between OC and OD varies smoothly. The distance constraint term is introduced to restrain the distance between the two-level layers of the one-level set function. The major contribution of the distance constraint term is that the relationship between OC and OD contours in a one-level set function is simpler to establish compared with using multiphase level set methods. Third
considering the complex structure of fundus images
the interference of blood vessels and lesions leads to sharp changes in contours
considerably affecting the accuracy of segmentation results. Therefore
we use the prior information that OC and OD always approach an ellipse shape to guide the segmentation process. In accordance with the geometric characteristics of OC and OD
the shape prior information of OC and OD is introduced to constrain the evolution of the active contour and realize the accurate segmentation of OC and OD. Lastly
we build an integral energy function by integrating the aforementioned three terms. Meanwhile
the traditional length penalty term is also included in the final integral energy function. The final energy function consists of four parts: local intensity information
distance constraint
prior information
and traditional length penalty terms. The segmentation of OC and OD is achieved by minimizing the final integral energy function using the gradient descent method. When the convergence condition of the level set function is satisfied
we obtain the final optimized level set function. We acquire OC and OD contours that correspond to the 0- and
k
-layers of the optimized level set function.
Result
2
The proposed method is validated using the CDRISHTI-GS1 dataset provided by the Aravind Eye Hospital of India with a true outline annotation of OC and OD. The dataset consists of 101 color retinal fundus images; each of which has a field of view of 30° and a resolution of 2 896×1 944 pixels. This dataset is primarily used to verify the robustness and effectiveness of OC and OD segmentation methods. The method proposed in this study achieves 81.04% of the average OD overlap rate
67.52% of the average OC overlap rate
0.719 of the OC F1-score
and 0.845 of the OD F1-score on the CDRISHTI-GS1 dataset. The method outperforms the OC and OD segmentation method based on the combination of shifted filter responses filter model
the shape prior constraint-based multiphase Chan-Vese model
and the cluster fusion-based level set method. We also show the visual results of the experimental process. The bias field and bias-corrected image are displayed to illustrate how the proposed method overcomes intensity inhomogeneity in fundus images. The histograms of the original image and the bias-corrected images are provided to present an intuitive result of the corrected image intensity.
Conclusion
2
This method successfully performs the segmentation of OC and OD regions by representing OC and OD contours with different level layers of a one-level set function. The intensity inhomogeneity of fundus images is effectively processed using the local information data term. The distance constraint term achieves a smooth change in distance between OC and OD contours. The ellipse shape prior constraint overcomes the blood vessels and lesions in fundus images
and the final segmented OC and OD contours are approximately two ellipse shapes. The experimental results show that the proposed method can simultaneously and accurately segment OC and OD regions. Moreover
it exhibits superior performance over other methods.
Almazroa A, Burman R, Raahemifar K and Lakshminarayanan V. 2015. Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. Journal of Ophthalmology, 2015: #180972[DOI: 10.1155/2015/180972 http://dx.doi.org/10.1155/2015/180972 ]
Chakravarty A and Sivaswamy J. 2017. Joint optic disc and cup boundary extraction from monocular fundus images. Computer Methods and Programs in Biomedicine, 147:51-61[DOI:10.1016/j.cmpb.2017.06.004]
Cheng J, Liu J, Xu Y W, Yin F S, Wong D W K, Tan N M, Tao D C, Cheng C Y, Aung T and Wong T Y. 2013. Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE Transactions on Medical Imaging, 32(6):1019-1032[DOI:10.1109/TMI.2013.2247770]
Dai B S, Wu X Q and Bu W. 2017. Optic disc segmentation based on variational model with multiple energies. Pattern Recognition, 64:226-235[DOI:10.1016/j.patcog.2016.11.017]
Esmaeili M, Rabbani H and Dehnavi A M. 2012. Automatic optic disk boundary extraction by the use of curvelet transform and deformable variational level set model. Pattern Recognition, 45(7):2832-2842[DOI:10.1016/j.patcog.2012.01.002]
Feng C L, Zhang S X, Zhao D Z and Li C M. 2016. Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets. Medical Physics, 43(6):2741-2755[DOI:10.1118/1.4947126]
Gao Y, Wu C D, Yu X S, Zhou W and Wu J H. 2018. Full-automatic optic disc boundary extraction based on active contour model with multiple energies. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E101.A(3): 658-661[DOI: 10.1587/transfun.E101.A.658 http://dx.doi.org/10.1587/transfun.E101.A.658 ]
Guo J P, Azzopardi G, Shi C Y, Jansonius N M and Petkov N. 2019. Automatic determination of vertical cup-to-disc ratio in retinal fundus images for glaucoma screening. IEEE Access, 7:8527-8541[DOI:10.1109/ACCESS.2018.2890544]
Hagiwara Y, Koh J E W, Tan J H, Bhandary S V, Laude A, Ciaccio E J, Tong L and Acharya U R. 2018. Computer-aided diagnosis of glaucoma using fundus images:a review. Computer Methods and Programs in Biomedicine, 165:1-12[DOI:10.1016/j.cmpb.2018.07.012]
Jonas J B, Bergua A, Schmitz-Valckenberg P, Papastathopoulos K I and Budde W M. 2000. Ranking of optic disc variables for detection of glaucomatous optic nerve damage. Investigative Ophthalmology&Visual Science, 41(7):1764-1773
Joshi G D, Sivaswamy J and Krishnadas S R. 2011. Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. IEEE Transactions on Medical Imaging, 30(6):1192-1205[DOI:10.1109/TMI.2011.2106509]
Khalid N E A, Noor N M and Ariff N M. 2014. Fuzzy c-Means (FCM) for optic cup and disc segmentation with morphological operation. Procedia Computer Science, 42:255-262[DOI:10.1016/j.procs.2014.11.060]
Li C M, Huang R, Ding Z H, Gatenby J C, Metaxas D N and Gore J C. 2011. A levelset method for image segmentation in the presence of intensity inhomogeneities with application to MRI. IEEE Transactions on Image Processing, 20(7):2007-2016[DOI:10.1109/TIP.2011.2146190]
Li C M, Kao C Y, Gore J C and Ding Z H. 2007. Implicit active contours driven by local binary fitting energy//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE: 1-7[DOI: 10.1109/CVPR.2007.383014 http://dx.doi.org/10.1109/CVPR.2007.383014 ]
Lotankar M, Noronha K and Koti J. 2015. Detection of optic disc and cup from color retinal images for automated diagnosis of glaucoma//Proceedings of 2015 IEEE UP Section Conference on Electrical Computer and Electronics (UPCON). Allahabad: IEEE: 1-6[DOI: 10.1109/UPCON.2015.7456741 http://dx.doi.org/10.1109/UPCON.2015.7456741 ]
Mittapalli P S and Kande G B. 2016. Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomedical Signal Processing and Control, 24:34-46[DOI:10.1016/j.bspc.2015.09.003]
Noor N M, Khalid N E A and Ariff N M. 2013. Optic cup and disc color channel multi-thresholding segmentation//Proceedings of 2013 IEEE International Conference on Control System, Computing and Engineering.Mindeb: IEEE: 530-534[DOI: 10.1109/ICCSCE.2013.6720022 http://dx.doi.org/10.1109/ICCSCE.2013.6720022 ]
Sivaswamy J, Krishnadas S R, Chakravarty A, Joshi G D, Ujjwal and Syed T A. 2015. A comprehensive retinal image dataset for the assessment of glaucoma from the optic nerve head analysis. JSM Biomedcal Imaging Data Papers, 2(1):#1004
Thakur N and Juneja M. 2018. Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42:162-189[DOI:10.1016/j.bspc.2018.01.014]
Thakur N and Juneja M. 2019. Optic disc and optic cup segmentation from retinal images using hybrid approach. Expert Systems with Applications, 127:308-322[DOI:10.1016/j.eswa.2019.03.009]
Wong D W K, Liu J, Lim J H, Jia X, Yin F, Li H and Wong T Y. 2008. Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI//Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vancouver: IEEE: 2266-2269[DOI: 10.1109/IEMBS.2008.4649648 http://dx.doi.org/10.1109/IEMBS.2008.4649648 ]
Zhang K H, Zhang L, Song H H and Zhang D. 2013. Reinitialization-free level set evolution via reaction diffusion. IEEE Transactions on Image Processing, 22(1):258-271[DOI:10.1109/TIP.2012.2214046]
Zhao H K, Chan T, Merriman B and Osher S. 1996. A variational level set approach to multiphase motion. Journal of Computational Physics, 127(1):179-195[DOI:10.1006/jcph.1996.0167]
Zheng S, Fan H J, Tang Y D and Wang Y. 2014. Automatic segmentation of optic disc and cup using multiphase active contour model in fundus images. Journal of Image and Graphics, 19(11):1604-1612
郑姗, 范慧杰, 唐延东, 王琰. 2014.多相主动轮廓模型的眼底图像杯盘分割.中国图象图形学报, 19(11):1604-1612[DOI:10.11834/jig.20141108]
Zheng Y J, Stambolian D, O'Brien J and Gee J C. 2013. Optic disc and cup segmentation from color fundus photograph using graph cut with priors//Proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). Nagoya: Springer: 75-82[DOI: 10.1007/978-3-642-40763-5_10 http://dx.doi.org/10.1007/978-3-642-40763-5_10 ]
Zhou H Y, Schaefer G, Liu T W and Lin F Q. 2010. Segmentation of optic disc in retinal images using an improved gradient vector flow algorithm. Multimedia Tools and Applications, 49(3):447-462[DOI:10.1007/s11042-009-0443-0]
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