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Retinal Vessels Segmentation by Improving Salient Region Combined with Sobel Operator Condition

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11814))

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Abstract

Medical images contribute greatly to help physicians identify abnormalities in the patient’s body in today’s health care. Retinal vessels are one of the effective methods for diagnosing diseases, such as: age-related macular degeneration, diabetes, hypertension, arteriosclerosis. However, manual analysis for retinal images is time-consuming and costly for ophthalmologists. In this paper, we proposed an approach for segmentation in retinal vessels by improving salient region map combined with Sobel mask. The algorithm includes two steps: superpixel detection and segmentation based on salient region map. The result of proposed method is better than the other methods.

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References

  1. Kaur, D., Kaur, Y.: Various image segmentation techniques: a review. Int. J. Comput. Sci. Mob. Comput. 3(5), 809–814 (2014)

    Google Scholar 

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

    Article  Google Scholar 

  3. Elena Martinez-Perez, M., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. Med. Image Anal. 11(1), 47–61 (2007)

    Article  Google Scholar 

  4. Ng, H.P., Ong, S.H., Foong, K.W.C., Goh, P.S., Nowinski, W.L.: Medical image segmentation using k-means clustering and improved watershed algorithm. In: Proceedings of IEEE Southwest Symposium on Image Analysis and Interpretation, pp. 61–65 (2006)

    Google Scholar 

  5. Saleh Al-amri, S., Kalyankar, N.V., Khamitkar, S.D.: Image segmentation by using threshold techniques. J. Comput. 2, 83–86 (2010)

    Google Scholar 

  6. Yin, Y., Adel, M., Bourenna, S.: Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation. Comput. Math. Methods Med. 2013, 1–16 (2013)

    Article  MathSciNet  Google Scholar 

  7. Li, Q., You, J., Zhang, D.: Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Syst. Appl. 39(9), 7600–7610 (2012)

    Article  Google Scholar 

  8. Fraz, M.M., et al.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538–2548 (2012)

    Article  Google Scholar 

  9. Manoj, S., Muralidharan, S.P.M., Sandeep, M.: Neural network-based classifier for retinal blood vessel segmentation. Int. J. Recent Trends Electr. Electron. Eng. 3, 44–53 (2013)

    Google Scholar 

  10. Marín, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: 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 (2011)

    Article  Google Scholar 

  11. Evelin Sujji, G., Lakshmi, Y.V.S., Wiselin Jiji, G.: MRI brain image segmentation based on thresholding. Int. J. Adv. Comput. Res. 3, 97–101 (2013)

    Google Scholar 

  12. Shan, H., Ma, J.: Curvelet-based geodesic snakes for image segmentation with multiple objects. J. Pattern Recogn. Lett. 31, 355–360 (2010)

    Article  Google Scholar 

  13. Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., Yang, G.: Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing 149, 708–717 (2015)

    Article  Google Scholar 

  14. Yuwei, W., Wang, Y., Jia, Y.: Adaptive diffusion flow active contours for image segmentation. Comput. Vis. Image Underst. 117, 1421–1435 (2013)

    Article  Google Scholar 

  15. Saadatmand-Tarzjan, M., Ghassemian, H.: Self-affine snake for medical image segmentation. Pattern Recogn. Lett. 59, 1–10 (2015)

    Article  Google Scholar 

  16. Zhang, R., Zhu, S., Zhou, Q.: A novel gradient vector flow snake model based on convex function for infrared image segmentation. Sensors 16(10), 1–17 (2016)

    Article  Google Scholar 

  17. Hamdi, M.A.: Modified algorithm marker-controlled watershed transform for image segmentation based on curvelet threshold. Can. J. Image Process. Comput. Vis. 2(8), 88–91 (2011)

    Google Scholar 

  18. Hore, S., et al.: An integrated interactive technique for image segmentation using stack based seeded region growing and thresholding. Int. J. Electr. Comput. Eng. 6(6), 2773–2780 (2016)

    Google Scholar 

  19. Han, J., Ngan, K.N., Li, M., Zhang, H.-J.: Unsupervised extraction of visual attention objects in color images. IEEE Trans. Circuits Syst. Video Technol. 16(1), 141–145 (2006)

    Article  Google Scholar 

  20. Permuter, H., Francos, J., Jermyn, I.: A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recogn. 39, 695–706 (2006)

    Article  Google Scholar 

  21. Liu, Q., Zou, B., Chen, J., Chen, Z.: Retinal vessel segmentation from simple to difficult. In: Proceedings of MICCAI Workshop on Ophthalmic Medical Image Analysis, pp. 57–64 (2016)

    Google Scholar 

  22. Barkana, B.D., Saricicek, I., Yildirim, B.: Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion. Knowl. Based Syst. 118, 165–176 (2017)

    Article  Google Scholar 

  23. Goferman, S., Zelnik-Manor, L., Tal, A.: Context-aware saliency detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(10), 1915–1926 (2012)

    Article  Google Scholar 

  24. Jiang, H., Wang, J., Yuan, Z., Liu, T., Zheng, N.: Automatic salient object segmentation based on context and shape prior. In: Proceedings of the British Machine Vision Conference, pp. 1–12 (2011)

    Google Scholar 

  25. Cheng, M.-M., Warrell, J., Lin, W.-Y., Zheng, S., Vineet, V., Crook, N.: Efficient salient region detection with soft image abstraction. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1529–1536 (2013)

    Google Scholar 

  26. Perazzi, F., Krähenbühl, P., Pritch, Y., Hornung, A.: Saliency filters: contrast based filtering for salient region detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 733–740 (2012)

    Google Scholar 

  27. Rezaee, K., Haddadnia, J., Tashk, A.: Optimized clinical segmentation of retinal blood vessels by using combination of adaptive filtering, fuzzy entropy and skeletonization. Appl. Soft Comput. 52, 937–951 (2017)

    Article  Google Scholar 

  28. Nageswara Reddy, P., Mohan Rao, C.P.V.N.J., Satyanarayana, Ch.: Brain MR image segmentation by modified active contours and contourlet transform. ICTACT J. Image Video Process. 8(2), 1645–1650 (2017)

    Article  Google Scholar 

  29. Roth, H.R., et al.: Deep learning and its application to medical image segmentation. Med. Imaging Technol. J. 36(2), 1–6 (2018)

    Google Scholar 

  30. Achanta, R., Estrada, F., Wils, P., Süsstrunk, S.: Salient region detection and segmentation. In: Gasteratos, A., Vincze, M., Tsotsos, J.K. (eds.) ICVS 2008. LNCS, vol. 5008, pp. 66–75. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79547-6_7

    Chapter  Google Scholar 

  31. Rosin, P.L.: A simple method for detecting salient regions. Pattern Recogn. 42(11), 2363–2371 (2009)

    Article  Google Scholar 

  32. Ao, H., Yu, N.: Edge saliency map detection with texture suppression. In: Proceedings of Sixth International Conference on Image and Graphics, pp. 309–313 (2011)

    Google Scholar 

  33. He, S., Pugeaulty, N.: Salient region segmentation. In: Computer Vision and Pattern Recognition, pp. 1–6 (2018)

    Google Scholar 

  34. Yao, Y.: Image segmentation based on Sobel edge detection. In: Proceedings of 5th International Conference on Advanced Materials and Computer Science, pp. 141–144 (2016)

    Google Scholar 

  35. https://www.isi.uu.nl/Research/Databases/DRIVE/. Accessed 19 Apr 2019

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Acknowledgement

This research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number B2019-20-05.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, VNU-HCM, Ho Chi Minh City, Vietnam

    Nguyen Thanh Binh, Vo Thi Hong Tuyet & Nguyen Mong Hien

  2. Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam

    Vo Thi Hong Tuyet

  3. Tra Vinh University, Tra Vinh, Vietnam

    Nguyen Mong Hien

  4. Faculty of Information Technology, Ho Chi Minh City University of Education, Ho Chi Minh City, Vietnam

    Nguyen Thanh Thuy

Authors
  1. Nguyen Thanh Binh
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  2. Vo Thi Hong Tuyet
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  3. Nguyen Mong Hien
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  4. Nguyen Thanh Thuy
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Corresponding author

Correspondence to Nguyen Thanh Binh .

Editor information

Editors and Affiliations

  1. Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler Universität Linz, Linz, Austria

    Josef Küng

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

  4. Telecommunications University, Nha Trang City, Vietnam

    Son Ha Bui

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Binh, N.T., Tuyet, V.T.H., Hien, N.M., Thuy, N.T. (2019). Retinal Vessels Segmentation by Improving Salient Region Combined with Sobel Operator Condition. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_39

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  • DOI: https://doi.org/10.1007/978-3-030-35653-8_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35652-1

  • Online ISBN: 978-3-030-35653-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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