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Segmenting retinal vessels with revised top-bottom-hat transformation and flattening of minimum circumscribed ellipse

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Abstract

Retinal vessel automatic segmentation plays a great important role for analyzing fundus pathologies like diabetes, retinopathy, and hypertension. In this paper, a novel unsupervised method to automatically extract the vessels from fundus images is introduced. The method proposed a new vessel enhancement approach that we called revised top-bottom-hat transformation for removing the bright lesions for further enhancing vessels in a fundus image, and provides a novel feature that we call flattening of minimum circumscribed ellipse for recognizing a vessel. This method was tested on two publicly available databases DRIVE and STARE, and achieved an average accuracy of 0.9446 and 0.9503, respectively. For pathological cases, the approach reached an accuracy of 0.9435 and 0.9439, respectively. The time complexity approaches (O(n)), which is significantly lower than the state-of-the-art method.

Graphical Abstract (GA)-Overview of the steps of the proposed algorithm

Step 1: Input. Input a fundus color image.

Step 2: Preprocess. The aim of process is to obtain gray image and to filter noise.

Step 3: Enhancement and amendment. For improving the segmentation accuracy, a new enhancement and amendment is applied for enhancing the vessels particularly thin vessels and removing the various disturbances.

Step 4: Blood vessel segmentation.

Step 4.1: Binarization. To identify the blood vessel, the threshold-based method is applied to gain binary images.

Step 4.2: Object decomposition. Before blood vessel recognition, we must decompose the binary image into some independent objects.

Step 4.3: Calculate the flattening. Calculate the flattening of each of objects.

Step 4.4: Blood vessel recognition. Blood vessels are identified by its flattening.

Step 5: Output. Output a blood vessel image

Graphical Abstract (GA)-Overview of the proposed approach. (a) Input Image. (b) Preprocessing. (c) Top-bottom-hat transformation. (d) Enhancement. (e) Blood vessel segmentation with different thresholds. (f) Blood vessels.

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References

  1. Abràmoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169–208

    Article  PubMed  PubMed Central  Google Scholar 

  2. Azzopardi G, Strisciuglio N, Vento M, Petkov N (2015) Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46–57

    Article  PubMed  Google Scholar 

  3. S M, Issac A, Dutta MK (2018) An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection. Int J Med Inform 110:52–70

    Article  Google Scholar 

  4. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 62(7):1738–1749

    Article  PubMed  Google Scholar 

  5. Bibiloni P, González-Hidalgo M, Massanet S (2018) A real-time fuzzy morphological algorithm for retinal vessel segmentation. J Real-Time Image Proc 5–6:1–14

    Google Scholar 

  6. Yin B, Li H, Sheng B, Hou X, Chen Y, Wu W, Li P, Shen R, Bao Y, Jia W (2015) Vessel extraction from non-fluorescein fundus images using orientation-aware detector. Med Image Anal 26(1):232–242

    Article  PubMed  Google Scholar 

  7. Pakter HM et al (2011) Computer-assisted methods to evaluate retinal vascular caliber: what are they measuring? Invest Ophthalmol Vis Sci 52(2):810–815

    Article  PubMed  Google Scholar 

  8. Khan KB, Khaliq AA, Jalil A, Shahid M (2018) A robust technique based on VLM and Frangi filter for retinal vessel extraction and denoising. PLoS One 13(2):e0192203

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Mastmeyer A, Fortmeier D, Handels H (2016) Efficient patient modeling for visuo-haptic VR simulation using a generic patient atlas. Comput Methods Progr Biomed 132(C):161–175

    Article  Google Scholar 

  10. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) Blood vessel segmentation methodologies in retinal images – a survey. Comput Methods Prog Biomed 108(1):407–433

    Article  CAS  Google Scholar 

  11. Moccia S, de Momi E, el Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms — review of methods, datasets and evaluation metrics. Comput Methods Prog Biomed 158:71–91

    Article  Google Scholar 

  12. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 62(7):1738–1749

    Article  PubMed  Google Scholar 

  13. Rahebi J, Hardalaç F (2014) Retinal blood vessel segmentation with neural network by using gray-level co-occurrence matrix-based features. J Med Syst 38(8):1–12

    Article  Google Scholar 

  14. Frangi AF et al (1998) Multiscale vessel enhancement filtering. Springer, Berlin Heidelberg, pp 130–137

    Google Scholar 

  15. Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26(10):1357–1365

    Article  PubMed  Google Scholar 

  16. Zhang J, Chen Y, Bekkers E, Wang M, Dashtbozorg B, Romeny BMH (2017) Retinal vessel delineation using a brain-inspired wavelet transform and random Forest. Pattern Recogn 69(C):107–123

    Article  Google Scholar 

  17. Soares JVB, Leandro JJG, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214–1222

    Article  PubMed  Google Scholar 

  18. Aslani S, Sarnel H (2016) A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Process Control 30:1–12

    Article  Google Scholar 

  19. Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA (2012) An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 59(9):2538–2548

    Article  PubMed  Google Scholar 

  20. Cheng E, du L, Wu Y, Zhu YJ, Megalooikonomou V, Ling H (2014) Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl 25(7):1779–1792

    Article  Google Scholar 

  21. Sinthanayothin C, Boyce JF, Cook HL, Williamson TH (1999) Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br J Ophthalmol 83(8):902–910

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35:1–1

    Article  Google Scholar 

  23. Ngo L, Han JH (2017) Multi-level deep neural network for efficient segmentation of blood vessels in fundus images. Electron Lett 53(16):1096–1098

    Article  Google Scholar 

  24. Yan Z, Yang X, Cheng KTT (2018) Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng 65(99):1–1

    Google Scholar 

  25. Roychowdhury S, Koozekanani DD, Parhi KK (2015) Iterative vessel segmentation of fundus images. IEEE Trans Biomed Eng 62(7):1738–1749

    Article  PubMed  Google Scholar 

  26. Roychowdhury S, Koozekanani DD, Parhi KK (2014) DREAM: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform 18(5):1717–1728

    Article  PubMed  Google Scholar 

  27. Lam BSY, Yan H (2008) A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Trans Med Imaging 27(2):237–246

    Article  PubMed  Google Scholar 

  28. Lam BSY, Gao Y, Liew AW (2010) General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 29(7):1369–1381

    Article  PubMed  Google Scholar 

  29. Vermeer KA, Vos FM, Lemij HG, Vossepoel AM (2004) A model based method for retinal blood vessel detection. Comput Biol Med 34(3):209–219

    Article  CAS  PubMed  Google Scholar 

  30. Zana F, Klein J (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010–1019

    Article  CAS  PubMed  Google Scholar 

  31. Miri MS, Mahloojifar A (2011) Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Trans Biomed Eng 58(5):1183–1192

    Article  PubMed  Google Scholar 

  32. Fang B, Hsu W, Lee ML (2003) Reconstruction of vascular structures in retinal images. in International Conference on Image Processing. ICIP 2003. Proceedings. 2003

  33. Abbasi-Sureshjani S et al (2017) Curvature integration in a 5D kernel for extracting vessel connections in retinal images. IEEE Trans Image Process 27(2):1–1

    Google Scholar 

  34. Chutatape O, Liu Z, Krishnan SM (1998) Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. p. 3144–3149 vol.6

  35. Zhou L, et al (1994) The detection and quantification of retinopathy using digital angiograms. in IEEE Transaction on Medical Imaging

  36. Nguyen UTV, Bhuiyan A, Park LAF, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn 46(3):703–715

    Article  Google Scholar 

  37. Adam H, Valentina K, Michae G (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210

    Article  Google Scholar 

  38. Kovács G, Hajdu A (2015) A self-calibrating approach for the segmentation of retinal vessels by template matching and contour reconstruction. Med Image Anal 29:24

    Article  PubMed  Google Scholar 

  39. Serra J (1982) Image analysis and mathematical morphology. Academic, London, U.K.

    Google Scholar 

  40. Liao M, Zhao YQ, Wang XH, Dai PS (2014) Retinal vessel enhancement based on multi-scale top-hat transformation and histogram fitting stretching. Opt Laser Technol 58(58):56–62

    Article  Google Scholar 

  41. Sandić D (1996) Mathematical morphology in image analysis. in Xi Conference on Applied Mathematics, Prim

  42. Fraz MM, Basit A, Barman SA (2013) Application of morphological bit planes in retinal blood vessel extraction. J Digit Imaging 26(2):274–286

    Article  CAS  PubMed  Google Scholar 

  43. Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509

    Article  PubMed  Google Scholar 

  44. Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40(4):438–445

    Article  PubMed  Google Scholar 

  45. Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269

    Article  CAS  PubMed  Google Scholar 

  46. Mendonça AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25(9):1200–1213

    Article  PubMed  Google Scholar 

  47. Palomera-Pérez MA et al (2010) Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans Inf Technol Biomed 14(2):500–506

    Article  PubMed  Google Scholar 

  48. You X, Peng Q, Yuan Y, Cheung YM, Lei J (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn 44(10–11):2314–2324

    Article  Google Scholar 

  49. Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka AR, Owen CG (2012) An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Prog Biomed 108(2):600–616

    Article  CAS  Google Scholar 

  50. Rouchdy Y, Cohen LD (2013) Geodesic voting for the automatic extraction of tree structures. Methods and applications ☆. Comput Vis Image Underst 117(10):1453–1467

    Article  Google Scholar 

  51. Dai P, Luo H, Sheng H, Zhao Y, Li L, Wu J, Zhao Y, Suzuki K (2015) A new approach to segment both main and peripheral retinal vessels based on gray-voting and Gaussian mixture model. PLoS One 10(6):e0127748

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Vega R, Sanchez-Ante G, Falcon-Morales LE, Sossa H, Guevara E (2015) Retinal vessel extraction using lattice neural networks with dendritic processing. Comput Biol Med 58(C):20–30

    Article  PubMed  Google Scholar 

  53. Maninis K, et al (2016) Deep retinal image understanding, in International Conference on Medical Image Computing and Computer-Assisted Intervention. p. 140–148

  54. Xu X, Ding W, Wang X, Cao R, Zhang M, Lv P, Xu F (2016) Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics. Sci Rep 6:34603

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bahadarkhan K, Khaliq AA, Shahid M (2016) A morphological Hessian based approach for retinal blood vessels segmentation and denoising using region based Otsu thresholding. PLoS One 117): p. e0158996

  56. Chen G et al (2017) Retina image vessel segmentation using a hybrid CGLI level set method. Biomed Res Int 2017:1–11

    Google Scholar 

  57. Wang W, Zhang J, Wu W, Zhou S (2018) An automatic approach for retinal vessel segmentation by multi-scale morphology and seed point tracking. J Med Imaging Health Inform 8(2):262–274(13)

    Article  Google Scholar 

  58. Marin D et al (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30(1):146–158

    Article  PubMed  Google Scholar 

  59. Orlando JI, Prokofyeva E, Blaschko MB (2016) A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans Biomed Eng 64(1):16–27

    Article  PubMed  Google Scholar 

  60. Jiang Z, Zhang H, Wang Y, Ko SB (2018) Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph 68(9):1–15

    Article  PubMed  Google Scholar 

  61. Oliveira A, Pereira S, Silva CA (2018) Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst Appl 113(11):229–242

    Article  Google Scholar 

  62. Asad AH, Hassaanien AE (2016) Retinal blood vessels segmentation based on bio-inspired algorithm. Intelligent Systems Reference Library 96(Chapter 8):181–215

  63. Bahadarkhan K, Khaliq AA, Shahid M (2016) A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based Otsu thresholding. PLoS One 11(7):e0162581

    Article  Google Scholar 

  64. Fraz MM, Basit A, Barman SA (2013) Application of morphological bit planes in retinal blood vessel extraction. J Digit Imaging 26(2):274–286

    Article  CAS  PubMed  Google Scholar 

  65. Bankhead P, Scholfield CN, McGeown JG, Curtis TM (2012) Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS One 7(3):e32435

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lam BSY, Gao Y, Liew AW (2010) General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 29(7):1369–1381

    Article  PubMed  Google Scholar 

  67. Yin Y, Adel M, Bourennane S (2013) Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation. Comput Math Methods Med 2013(1):585–593

    Google Scholar 

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Acknowledgments

We would like to thank JJ Staal and Hoover and their colleagues for providing their databases.

Funding

This work is partially supported by the Chongqing Research Program of Basic Research and Frontier Technology under grant nos. cstc2015jcyjBX0019 and cstc2016jcyjA0145, in part by the Scientific Research Fund of Chongqing Municipal Education Commission under grant no. KJ1711268, and in part by the Scientific Research Fund of Chongqing University of Arts and Sciences under grant no. Z2018RJ08.

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Authors and Affiliations

  1. School of Software Engineering, Chongqing University of Arts and Sciences, Yongchuan, Chongqing, 402160, China

    Weihua Wang & Zhangping Hu

  2. Business College, Southwest University, Rongchang, Chongqing, 402460, China

    Weiqing Wang

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  1. Weihua Wang
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Correspondence to Weiqing Wang.

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Wang, W., Wang, W. & Hu, Z. Segmenting retinal vessels with revised top-bottom-hat transformation and flattening of minimum circumscribed ellipse. Med Biol Eng Comput 57, 1481–1496 (2019). https://doi.org/10.1007/s11517-019-01967-2

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