基于双字典学习的眼底图像血管分割
|
摘要
为辅助诊断眼底疾病和部分心血管疾病,本文提出一种基于双字典学习和多尺度线状结构检测的眼底图像血管分割方法。首先在HSV颜色空间利用伽马矫正均衡眼底图像的亮度,并在Lab颜色空间采用CLAHE算法提升图像对比度,再采用多尺度线状结构检测算法突出血管结构得到增强后的特征图像;然后利用K-SVD算法训练特征图像块和对应的手绘血管标签图像块,得到表示字典和分割字典,采用表示字典得到新输入特征图像块的重构稀疏系数,由该系数和分割字典获得血管图像块;最后进行图像块拼接、噪声去除和空洞填充等后处理得到最终分割结果。在DRIVE和HRF数据库测试,利用准确率、特异度、敏感度等八种评估指标来检验分割性能。其中,平均准确率分别达0.958 2和0.951 5,平均特异度分别达到0.982 6和0.967 1,平均敏感度分别达到0.709 5和0.762 6,表明该方法具有较好的分割性能和通用性。
In order to assist the diagnoses of fundus diseases and some cardiovascular diseases,this paper proposes a fundus image blood vessel segmentation method via joint dictionary learning and multi-scale line structure detection.Firstly,brightness is adjusted and balanced by gamma correction in HSV color space,contrast is improved via CLAHE algorithm in Lab color space,and multi-scale line structure detection algorithm is used to enhance the blood vessel structures and get the feature maps.Then,the representation dictionary and segmentation dictionary are trained simultaneously by K-SVD algorithm from the feature blocks and its corresponding manually annotated vessel label blocks.The reconstructed sparse coefficients of newly input enhanced feature blocks are obtained with the representation dictionary,and the blood vessel blocks are segmented by these coefficients and segmentation dictionary.Finally,the blood vessel result is obtained via image blocks stitching,noise removal and hole filling algorithms.Our method is tested on DRIVE and HRF databases to evaluate the segmentation performance in accuracy,sensitivity,specificity and other five metrics.The average accuracy rate reaches 0.958 2 and 0.951 5 respectively,the average specificity reaches 0.982 6 and 0.967 1 respectively,the average sensitivity reaches 0.709 5 and 0.762 6 respectively,which indicates that our method has good segmentation performance and versatility.
In order to assist the diagnoses of fundus diseases and some cardiovascular diseases,this paper proposes a fundus image blood vessel segmentation method via joint dictionary learning and multi-scale line structure detection.Firstly,brightness is adjusted and balanced by gamma correction in HSV color space,contrast is improved via CLAHE algorithm in Lab color space,and multi-scale line structure detection algorithm is used to enhance the blood vessel structures and get the feature maps.Then,the representation dictionary and segmentation dictionary are trained simultaneously by K-SVD algorithm from the feature blocks and its corresponding manually annotated vessel label blocks.The reconstructed sparse coefficients of newly input enhanced feature blocks are obtained with the representation dictionary,and the blood vessel blocks are segmented by these coefficients and segmentation dictionary.Finally,the blood vessel result is obtained via image blocks stitching,noise removal and hole filling algorithms.Our method is tested on DRIVE and HRF databases to evaluate the segmentation performance in accuracy,sensitivity,specificity and other five metrics.The average accuracy rate reaches 0.958 2 and 0.951 5 respectively,the average specificity reaches 0.982 6 and 0.967 1 respectively,the average sensitivity reaches 0.709 5 and 0.762 6 respectively,which indicates that our method has good segmentation performance and versatility.
引文
[1] Abràmoff M D,Garvin M K,Sonka M.Retinal imaging and image analysis[J].IEEE Reviews in Biomedical Engineering,2010,3:169-208.
[2] Cheung N,Mitchell P,Wong T Y.Diabetic retinopathy[J].The Lancet,2010,9735(376):124-136.
[3] Fraz M M,Remagnino P,Hoppe A,et al.Blood vessel segmentation methodologies in retinal images-A survey[J].Computer Methods and Programs in Biomedicine,2012,108(1):407-433.
[4] Annunziata R, Garzelli A, Ballerini L, et al.Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation[J].IEEE Journal of Biomedical and Health Informatics,2016,20(4):1129-1138.
[5] Zhao Y,Zhao J,Yang J,et al.Saliency driven vasculature segmentation with infinite perimeter active contour model[J].Neurocomputing,2017,259(11):201-209.
[6] Zhao Y,Zhao Y,Zheng Y,et al.Automatic 2D/3D vessel enhancement in multiple modality images using a weighted symmetry filter[J].IEEE Transactions on Medical Imaging,2018,37(2):438-450.
[7] Zhang J,Dashtbozorg B,Bekkers E,et al.Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores[J].IEEE Transactions on Medical Imaging,2016,35(12):2631-2644.
[8] Shah S A A,Tong B T,Faye I,et al.Blood vessel segmentation in color fundus images based on regional and Hessian features[J].Graefes Archive for Clinical and Experimental Ophthalmology,2017,255(8):1525-1533.
[9] CAI Yi-heng,GAO Xu-rong,QIU Chang-yan,et al.Retinal vessel segmentation method with efficient hybrid features fusion[J].Journal of Electronics and Information Technology,2017,39(8):1956-1963.蔡轶珩,高旭蓉,邱长炎,等.一种混合特征高效融合的视网膜血管分割方法[J].电子与信息学报,2017,39(8):1956-1963.
[10] ZHU Cheng-zhang,CUI Jin-kai,ZOU Bei-ji,et al.Retinal vessel segmentation based on multiple feature fusion and random forest[J].Journal of Computer-Aided Design and Computer Graphics,2017,29(4):584-592.朱承璋,崔锦恺,邹北骥,等.基于多特征融合和随机森林的视网膜血管分割[J].计算机辅助设计与图形学学报,2017,29(4):584-592.
[11] Li Q,Feng B,Xie L,et al.A cross-modality learning approach for vessel segmentation in retinal images[J].IEEE Transactions on Medical Imaging,2016,35(1):109-118.
[12] Zhou M,Jin K,Wang S,et al.Color retinal image enhancement based on luminosity and contrast adjustment[J].IEEE Transactions on Biomedical Engineering,2018,65(3):521-527.
[13] Aharon M,Elad M,Bruckstein A.K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[14] Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition[C].Conference Record of The Twenty-Seventh Asilomar Conference,Signals,Systems and Computers:IEEE,2002,40-44.
[15] 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.
[16] Odstrcilik J,Kolar R,Budai A,et al.Retinal vessel segmentation by improved matched filtering:evaluation on a new high-resolution fundus image database[J].IET Image Processing,2013,7(4):373-383.
[17] Orlando J I,Prokofyeva E,Blaschko M B.A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images[J].IEEE Transactions on Biomedical Engineering,2016,64(1):16-27.
[18] Fraz M M,Remagnino P,Hoppe A,et al.Retinal vessel extraction using first-order derivative of Gaussian and morphological processing[J].Advances in Visual Computing,2011,6938:410-420.
[19] 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.
[20] Fraz M M,Basit A,Barman S A.Application of morphological bit planes in retinal blood vessel extraction[J].Journal of Digital Imaging,2013,26(2):274-286.
[21] Vega R,Sanchez-Ante G,Falcon-Morales L E,et al.Retinal vessel extraction using lattice neural networks with dendritic processing[J].Computers in Biology and Medicine,2015,58(1):20-30.
[22] Aslani S,Sarnel H.A new supervised retinal vessel segmentation method based on robust hybrid features[J].Biomedical Signal Processing and Control,2016,30:1-12.
[2] Cheung N,Mitchell P,Wong T Y.Diabetic retinopathy[J].The Lancet,2010,9735(376):124-136.
[3] Fraz M M,Remagnino P,Hoppe A,et al.Blood vessel segmentation methodologies in retinal images-A survey[J].Computer Methods and Programs in Biomedicine,2012,108(1):407-433.
[4] Annunziata R, Garzelli A, Ballerini L, et al.Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation[J].IEEE Journal of Biomedical and Health Informatics,2016,20(4):1129-1138.
[5] Zhao Y,Zhao J,Yang J,et al.Saliency driven vasculature segmentation with infinite perimeter active contour model[J].Neurocomputing,2017,259(11):201-209.
[6] Zhao Y,Zhao Y,Zheng Y,et al.Automatic 2D/3D vessel enhancement in multiple modality images using a weighted symmetry filter[J].IEEE Transactions on Medical Imaging,2018,37(2):438-450.
[7] Zhang J,Dashtbozorg B,Bekkers E,et al.Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores[J].IEEE Transactions on Medical Imaging,2016,35(12):2631-2644.
[8] Shah S A A,Tong B T,Faye I,et al.Blood vessel segmentation in color fundus images based on regional and Hessian features[J].Graefes Archive for Clinical and Experimental Ophthalmology,2017,255(8):1525-1533.
[9] CAI Yi-heng,GAO Xu-rong,QIU Chang-yan,et al.Retinal vessel segmentation method with efficient hybrid features fusion[J].Journal of Electronics and Information Technology,2017,39(8):1956-1963.蔡轶珩,高旭蓉,邱长炎,等.一种混合特征高效融合的视网膜血管分割方法[J].电子与信息学报,2017,39(8):1956-1963.
[10] ZHU Cheng-zhang,CUI Jin-kai,ZOU Bei-ji,et al.Retinal vessel segmentation based on multiple feature fusion and random forest[J].Journal of Computer-Aided Design and Computer Graphics,2017,29(4):584-592.朱承璋,崔锦恺,邹北骥,等.基于多特征融合和随机森林的视网膜血管分割[J].计算机辅助设计与图形学学报,2017,29(4):584-592.
[11] Li Q,Feng B,Xie L,et al.A cross-modality learning approach for vessel segmentation in retinal images[J].IEEE Transactions on Medical Imaging,2016,35(1):109-118.
[12] Zhou M,Jin K,Wang S,et al.Color retinal image enhancement based on luminosity and contrast adjustment[J].IEEE Transactions on Biomedical Engineering,2018,65(3):521-527.
[13] Aharon M,Elad M,Bruckstein A.K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J].IEEE Transactions on Signal Processing,2006,54(11):4311-4322.
[14] Pati Y C, Rezaiifar R, Krishnaprasad P S. Orthogonal matching pursuit:recursive function approximation with applications to wavelet decomposition[C].Conference Record of The Twenty-Seventh Asilomar Conference,Signals,Systems and Computers:IEEE,2002,40-44.
[15] 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.
[16] Odstrcilik J,Kolar R,Budai A,et al.Retinal vessel segmentation by improved matched filtering:evaluation on a new high-resolution fundus image database[J].IET Image Processing,2013,7(4):373-383.
[17] Orlando J I,Prokofyeva E,Blaschko M B.A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images[J].IEEE Transactions on Biomedical Engineering,2016,64(1):16-27.
[18] Fraz M M,Remagnino P,Hoppe A,et al.Retinal vessel extraction using first-order derivative of Gaussian and morphological processing[J].Advances in Visual Computing,2011,6938:410-420.
[19] 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.
[20] Fraz M M,Basit A,Barman S A.Application of morphological bit planes in retinal blood vessel extraction[J].Journal of Digital Imaging,2013,26(2):274-286.
[21] Vega R,Sanchez-Ante G,Falcon-Morales L E,et al.Retinal vessel extraction using lattice neural networks with dendritic processing[J].Computers in Biology and Medicine,2015,58(1):20-30.
[22] Aslani S,Sarnel H.A new supervised retinal vessel segmentation method based on robust hybrid features[J].Biomedical Signal Processing and Control,2016,30:1-12.