基于马尔可夫随机场的眼底图像血管分割研究
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摘要
眼底图像血管分割是医用图像分割中较为复杂的一种,在目前的研究中存在分割精度低、效率不高等问题。提出基于马尔可夫随机场的眼底图像血管分割算法,根据眼底图像的特点构建马尔可夫随机场模型,提取H通道作为特征场参数,利用最大后验准则完成标号场更新,最终实现对视网膜血管的分割。算法通过眼底图像数据库DRIVE进行测试,结果表明:该算法平均准确度为0.954 6,平均敏感度为0.899 9,平均特异度为0.957 1,具有很好的分割效果,且运行稳定,计算方便快捷,具有鲁棒性。
Vessel segmentation of fundus image is a relatively complex method in medical image segmentation. There are some problems in current research, such as low segmentation accuracy and low efficiency. This paper proposed vessel segmentation algorithm of fundus image based on the Markov random field. According to the characteristics of fundus image, a Markov random field model was constructed. H channel was extracted as the parameter of feature field, and the labeling field was updated by using the maximum posteriori criterion. Finally, the retinal blood vessels were segmented. The algorithm was tested by a fundus image database DRIVE. The results show that the average accuracy, sensitivity and specificity of the algorithm are 0.954 6, 0.899 9 and 0.957 1 respectively. The algorithm has good segmentation effect, stable operation, convenient calculation and robustness.
Vessel segmentation of fundus image is a relatively complex method in medical image segmentation. There are some problems in current research, such as low segmentation accuracy and low efficiency. This paper proposed vessel segmentation algorithm of fundus image based on the Markov random field. According to the characteristics of fundus image, a Markov random field model was constructed. H channel was extracted as the parameter of feature field, and the labeling field was updated by using the maximum posteriori criterion. Finally, the retinal blood vessels were segmented. The algorithm was tested by a fundus image database DRIVE. The results show that the average accuracy, sensitivity and specificity of the algorithm are 0.954 6, 0.899 9 and 0.957 1 respectively. The algorithm has good segmentation effect, stable operation, convenient calculation and robustness.
引文
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[20] Nguyen U T V,Bhuiyan A,Park L A F,et al.An effective retinal blood vessel segmentation method using multi-scale line detection[J].Pattern Recognition,2013,46(3):703-715.
[2] 郝聚涛,赵晶晶,陈庆奎,等.基于多属性的空间连续模糊聚类算法的血管分割[J].中国图象图形学报,2009,14(8):1643-1649.
[3] 程明,黄晓阳,黄绍辉,等.定向区域生长算法及其在血管分割中的应用[J].中国图象图形学报,2011,16(1):44-49.
[4] 段彦华.眼底图像中视杯分割和硬性渗出物检测算法研究[D].北京:北京工业大学,2016.
[5] 姚畅,陈后金,李居朋.基于过渡区提取的视网膜血管分割方法[J].电子学报,2008,36(5):974-978.
[6] 王文涛,罗晓曙,阎晨阳.基于灰度迭代阈值PCNN的眼底图像血管分割[J].计算机工程与应用,2018,54(20):202-206.
[7] Araújo T,Mendon?a A M,Campilho A,et al.Parametric model fitting-based approach for retinal blood vessel caliber estimation in eye fundus images[J].PLoS ONE,2018,13(4):e0194702.
[8] Liskowski P,Krawiec K.Segmenting retinal blood vessele with deep neural networks[J].IEEE Transactions on Medical Imaging,2016,35(11):2369-2380.
[9] 杨鹏.基于马尔科夫随机场的纹理图像分割研究[D].哈尔滨:哈尔滨工程大学,2015.
[10] 李旭超,朱善安.图像分割中的马尔可夫随机场方法综述[J].中国图象图形学报,2007,12(5):789-798.
[11] 吴非.基于模糊聚类的数据库空值估计研究[D].南京:南京航空航天大学,2016.
[12] Marin D,Aquino A,Gegundez-Arias M E,et al.A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features[J].IEEE Transactions on Medical Imaging,2011,30(1):146-158.
[13] Staal J,Abramoff M D,Niemeijer M,et al.Ridge-based vessel segmentation in color images of the retina[J].IEEE Transactions on Medical Imaging,2004,23(4):501-509.
[14] Soares J V,Leandro J G,Cesar R M,et al.Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification[J].IEEE Transactions on Medical Imaging,2006,25(9):1214-1222.
[15] Mendonca A M,Campilho A.Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction[J].IEEE Transactions on Medical Imaging,2006,25(9):1200-1213.
[16] Budai A,Bock R,Maier A,et al.Robust Vessel Segmentation in Fundus Images[J].International Journal of Biomedical Imaging,2013,2013:154860.
[17] Udayakumar R,Khanaa V,Saravanan T,et al.Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction[J].Middle East Journal of Scientific Research,2013,58(5):1183-1192.
[18] Hassanien A E,Emary E,Zawbaa H M.Retinal blood vessel localization approach based on bee colony swarm optimization,fuzzy c-means and pattern search[J].Journal of Visual Communication and Image Representation,2015,31:186-196.
[19] Xu X,Ding W,Wang X,et al.Smartphone-Based Accurate Analysis of Retinal Vasculature towards Point-of-Care Diagnostics[J].Scientific Reports,2016,6:34603.
[20] Nguyen U T V,Bhuiyan A,Park L A F,et al.An effective retinal blood vessel segmentation method using multi-scale line detection[J].Pattern Recognition,2013,46(3):703-715.