• Acta Optica Sinica
  • Vol. 40, Issue 10, 1010001 (2020)
Daxiang Li1、2 and Zhen Zhang1、*
Author Affiliations
  • 1College of Communication and Infornation Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
  • 2Key Laboratory of Ministry of Public Security, Electronic Information Field Inspection and Application Technology, Xi'an, Shaanxi 710121, China
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    DOI: 10.3788/AOS202040.1010001 Cite this Article Set citation alerts
    Daxiang Li, Zhen Zhang. Improved U-Net Segmentation Algorithm for the Retinal Blood Vessel Images[J]. Acta Optica Sinica, 2020, 40(10): 1010001 Copy Citation Text show less
    References

    [1] 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, 46, 703-715(2013).

    [2] Roychowdhury S, Koozekanani D D, Parhi K K. Iterative vessel segmentation of fundus images[J]. IEEE Transactions on Biomedical Engineering, 62, 1738-1749(2015).

    [3] Bekkers E, Duits R, Berendschot T et al. A multi-orientation analysis approach to retinal vessel tracking[J]. Journal of Mathematical Imaging and Vision, 49, 583-610(2014).

    [4] Yin Y, Adel M, Bourennane S. Retinal vessel segmentation using a probabilistic tracking method[J]. Pattern Recognition, 45, 1235-1244(2012).

    [5] 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, 25, 1200-1213(2006).

    [6] Miri M S, Mahloojifar A. Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction[J]. IEEE Transactions on Biomedical Engineering, 58, 1183-1192(2011).

    [7] Zhao Y Q, Wang X H, Wang X F et al. Retinal vessels segmentation based on level set and region growing[J]. Pattern Recognition, 47, 2437-2446(2014).

    [8] Zhao Y T, Rada L, Chen K et al. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images[J]. IEEE Transactions on Medical Imaging, 34, 1797-1807(2015).

    [9] Gooya A, Liao H, Matsumiya K et al. Avariational method for geometric regularization of vascular segmentation in medical images[J]. IEEE Transactions on Image Processing, 17, 1295-1312(2008).

    [10] Xu X Y, Niemeijer M, Song Q et al. Vessel boundary delineation on fundus images using graph-based approach[J]. IEEE Transactions on Medical Imaging, 30, 1184-1191(2011).

    [11] De J, Cheng L, Zhang X W et al. A graph-theoretical approach for tracing filamentary structures in neuronal and retinal images[J]. IEEE Transactions on Medical Imaging, 35, 257-272(2016).

    [12] Câmara Neto L. Ramalho G L B, Rocha Neto J F S, et al. An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images[J]. Expert Systems with Applications, 78, 182-192(2017).

    [13] Orlando J I, Prokofyeva E, Blaschko M B. Adiscriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images[J]. IEEE Transactions on Biomedical Engineering, 64, 16-27(2017).

    [14] Wang X H, Jiang X D, Ren J F. Blood vessel segmentation from fundus image by a cascadeclassification framework[J]. Pattern Recognition, 88, 331-341(2019).

    [15] Liskowski P, Krawiec K. Segmenting retinal blood vessels with deep neural networks[J]. IEEE Transactions on Medical Imaging, 35, 2369-2380(2016).

    [16] Oliveira A, Pereira S, Silva C A. Retinal vessel segmentation based on fully convolutional neural networks[J]. Expert Systems with Applications, 112, 229-242(2018).

    [17] Hu K, Zhang Z Z, Niu X R et al. Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function[J]. Neurocomputing, 309, 179-191(2018).

    [18] Yan Z Q, Yang X, Cheng K T. A three-stage deep learning model for accurate retinal vessel segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 23, 1427-1436(2019).

    [19] Wu C Y, Yi B S, Zhang Y G et al. Retinal vessel image segmentation based on improved convolutional neural network[J]. Acta Optica Sinica, 38, 1111004(2018).

    [20] Yan Z Q, Yang X, Cheng K T. Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation[J]. IEEE Transactions on Biomedical Engineering, 65, 1912-1923(2018).

    [21] Zheng T Y, Tang C, Lei Z K. Multi-scale retinal vessel segmentation based on fully convolutional neural network[J]. Acta Optica Sinica, 39, 0211002(2019).

    [22] Feng S T, Zhuo Z S, Pan D et al[2020-01-02]. 2019-04-23) https://doi.org/10.1016/j.neucom., 10, 098(2018).

    [23] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 15523970(2015).

    [24] Chen L C, Papandreou G, Kokkinos I et al. DeepLab:semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

    [26] Liang L M, Sheng X Q, Lan Z M et al. U-shaped retinal vessel segmentation algorithm based on adaptive scale information[J]. Acta Optica Sinica, 39, 0810004(2019).

    [27] 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, 23, 501-509(2004).

    [28] Chen M M, Xiong X L, Zhang Y et al. A new method for retinal fundus image enhancement[J]. Journal of Chongqing Medical University, 39, 1087-1090(2014).

    [29] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. [C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 15802053(2015).

    [30] Azzopardi G, Strisciuglio N, Vento M et al. Trainable COSFIRE filters for vessel delineation with application to retinal images[J]. Medical Image Analysis, 19, 46-57(2015).

    [31] Wang S L, Yin Y L, Cao G B et al. Hierarchical retinal blood vessel segmentation based on feature and ensemble learning[J]. Neurocomputing, 149, 708-717(2015).

    Daxiang Li, Zhen Zhang. Improved U-Net Segmentation Algorithm for the Retinal Blood Vessel Images[J]. Acta Optica Sinica, 2020, 40(10): 1010001
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