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Segmentation of Images Using Watershed and MSER: A State-of-the-Art Review

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Recent Advances in Intelligent Systems and Smart Applications

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 295))

Abstract

The success of watershed transform in image processing and image analysis domain is due to its ability to induce closed object boundaries and needs less computation time with reference to other segmentation methods. This review delivers a thorough study of conventional image segmentation methods like watershed algorithm, marker-controlled watershed and the Maximally Stable Extremal Region (MSER) algorithm using Magnetic Resonance Images (MRI). This study determines the major accomplishments in the performance metrics of the relevant algorithms in the three areas such as tracking of fibres, licence plate, and faces. In addition, this review examines the key outcomes and focuses on the lessons learned and thus forms the foundation for future research. The results of the literature indicate that the MSER-based procedures outperforms well as compared with the other methods. The experimental results of the applications shows that MSER provides improved speed and stability.

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References

  1. Beucher, S., Lantuéjoul, C.: Use of Watersheds in Contour Detection. Workshop Published (1979)

    Google Scholar 

  2. Vincent, L., Beucher, S.: The morphological approach to segmentation: an introduction.Technical report, School of Mines, CMM, Paris (1989)

    Google Scholar 

  3. Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13(6), 583–598 (1991)

    Article  Google Scholar 

  4. Meijster, A., Roerdink, J.B.T.M.: A disjoint set algorithm for the watershed transform. In: Proceedings EUSIPCO ’98, IX European Signal Processing Conference, pp. 1665–1668 (1998)

    Google Scholar 

  5. Haris, K., Efstratiadis, S.N., Maglaveras, N., Katsaggelos, A.K.: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7(12), 1684–1699 (1998)

    Google Scholar 

  6. Lotufo, R., Falcao, A.: The ordered queue and the optimality of the watershed approaches. Math. Morphol. Appl. Image Signal Process. 18, 341–350 (2000)

    MATH  Google Scholar 

  7. Chen, T.: Gushing and Immersion Alternative Watershed Algorithm, pp. 246–248 (2001)

    Google Scholar 

  8. Rambabu, C., Rathore, T., Chakrabarti, I.: A new watershed algorithm based on hill climbing technique for image segmentation. In: TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, Vol. 4, pp. 1404–1408 (2003)

    Google Scholar 

  9. Shen, W.C., Chang, R.F.: A nearest neighbor graph based watershed algorithm, pp. 6300–6303 (2005)

    Google Scholar 

  10. Rambabu, C., Chakrabarti, I.: An efficient hill climbing-based watershed algorithm and its prototype hardware architecture. J. Signal Process. Syst. 52(3), 281–295 (2008)

    Article  Google Scholar 

  11. Beucher, S.: The watershed transform applied to image segmentation. In: Proceedings of the Pfefferkorn Conference on Signal and Image Processing in Microscopy and Microanalysis, pp. 299–314 (1991)

    Google Scholar 

  12. Haris, K., Efstratiadis, S.N., Maglaveras, N., Katsaggelos, A.K.: Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7(12) (1998)

    Google Scholar 

  13. Meyer, F., Beucher, S.: Morphological segmentation. J. Visual Commun. Image Representation 1(1), 21–46 (1990) (Academic Press)

    Google Scholar 

  14. Grau, V., Mewes, A.U.J., Alcaniz, M., Kikinis, R., Warfield, S.K.: Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med. Imaging 23(4), 447–458, 0278-0062 (2004)

    Google Scholar 

  15. Tek, F.B., Dempster, A.G., Kale, I.: Noise sensitivity of watershed segmentation for different connectivity: experimental study. Electron. Lett. 40(21), 1332–1333 (2004). 0013-5194. Dept. of Electron. Syst., Univ. of Westminster, London, UK

    Google Scholar 

  16. Jackway, P.T.: Gradient watersheds in morphological scale space. IEEE Trans. Image Proc. 5, 913–921 (1999)

    Article  Google Scholar 

  17. Weickert, J.: Efficient image segmentation using partial differential equations and morphology. Pattern Recogn. 34, 1813–1824 (2001)

    Article  Google Scholar 

  18. Jung, C.R., Scharcanski, J.: Robust watershed segmentation using wavelets. Image Vision Comput. 23, 661–669 (2005)

    Article  Google Scholar 

  19. Cates, J.E., Whitaker, R.T., Jones, G.M.: Case study: an evaluation of user-assisted hierarchical watershed segmentation. Med. Image Anal. ITK Open Science-Combining Open Data Open Source Softw. Med. Image Anal. Insight Toolkit 9(6), 566–578 (2005)

    Google Scholar 

  20. Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. BMVC 2002, 384–396 (2002)

    Google Scholar 

  21. Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. Int. Conf. Comput. Vision 2, 1470–1477 (2003)

    Article  Google Scholar 

  22. Obdrzalek, S.J.: Object recognition using local affine frames on distinguished regions. In: British Machine Vision Conference, Vol. 1, pp. 113–122 (2002)

    Google Scholar 

  23. Donoser, M., Bischof, H.: Efficient maximally stable extremal region (mser) tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 553–560 (2006)

    Google Scholar 

  24. Donoser, M., Bischof, H.: 3d segmentation by maximally stable volumes (msvs). In: ICPR 2006: Proceedings of the 18th International Conference on Pattern Recognition, pp. 63–66 (2006)

    Google Scholar 

  25. Chen, H., Tsai, S.S., Schroth, G., Chen, D.M., Grzeszczuk, R., Girod, B.: Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 118th IEEE International Conference on Image Processing (ICIP) (2011)

    Google Scholar 

  26. Gao, Y., Shan, X., Hu, Z., Wang, D., Li, Y., Tian, X.: Extended compressed tracking via random projection based on MSERs and online LSSVM learning. Pattern Recogn. 59, 245–254 (2016)

    Article  Google Scholar 

  27. Kristensen, F., MacLean, W.J.: Fpga real-time extraction of maximally-stable extremal regions. In IEEE International Symposium on Circuits and Systems (2007)

    Google Scholar 

  28. Vedaldi, A.: An Implementation of Multi-dimensional Maximally Stable Extremal Regions. Technical Report, Feb 7 (2007)

    Google Scholar 

  29. Forssen, P.: Maximally stable colour regions for recognition and matching. In: IEEE Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  30. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vision 59, 167–181 (2004)

    Google Scholar 

  31. Hafts, S.N., Maglaveras, E.N., Pappas, C.: Hybrid image segmentation using watersheds. In: SP1E Proceedings of Visual Communications and Image Processing ’96, Vol. 27, pp. 1140–1151, Orlando, Florida, U.S.A. (1996)

    Google Scholar 

  32. Salembier: Morphological multiscale segmentation for image coding. Signal Process. 38, 359–386 (1994)

    Google Scholar 

  33. Kaleem, M., Sanaullah, M., Hussain, M.A., Jaffar, M.A., Choi, T.S.: Segmentation of brain tumor tissue using marker controlled watershed transform method. Commun. Comput. Inf. Sci. 281, 222–227 (2012)

    Google Scholar 

  34. Shafarenko, L., Petrou, M., Kittler, J.: Automatic watershed segmentation of randomly textured color images. IEEE Trans. Image Process. 6(11), 1530–1544 (1997)

    Article  Google Scholar 

  35. Moga, A.N., Gabbouj, M.: Parallel image component labelling with watershed transformation. IEEE Trans. Pattern Anal. Mach. Intell. 19(5), 441–450 (1997)

    Article  Google Scholar 

  36. Zhu, H., Sheng, J., Zhang, F., Zhou, J., Wang, J.: Improved maximally stable extremal regions based method for the segmentation of ultrasonic liver images. Multi-media Tools Appl

    Google Scholar 

  37. Chevrefils, C., Cheriet, F., Aubin, C.-E., Grimard, G.: Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from MR images. IEEE Trans. Inf. Technol. Biomed. 13(4), 608–620 (2009)

    Google Scholar 

  38. Chevrefils, F.C.: Watershed segmentation of intervertebral disk and spinal canal from MRI images. In: Proc. Int. Conf. Image Anal. Recognit., no. 3, pp. 1017–1027 (2007)

    Google Scholar 

  39. Abdelfadeel, M.A., ElShehaby, S., Abougabal, M.S.: Automatic segmentation of left ventricle in cardiac MRI using maximally stable extremal regions. In: Biomedical Engineering Conference (CIBEC), pp. 145–148 (2014)

    Google Scholar 

  40. Khan, M.A., Lali, I.U., Rehman, A., Ishaq, M., Sharif, M., Saba, T., Zahoor, S: Brain tumor detection and classification: a framework of marker‐based watershed algorithm and multilevel priority features selection. Microsc. Res. Tech. 82(6), 909–922 (2019)

    Google Scholar 

  41. Grau, V., Mewes, A.U.J., Alcañiz, M., Kikinis, R., Warfield, S.K.: Improved watershed transform for medical image segmentation using prior information. IEEE Trans. Med. Imaging 23(4), 447–458 (2004)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, College of Engineering, Attingal, Thiruvananthapuram, India

    M. Leena Silvoster

  2. Information Technology Department, Noorul Islam University, Tamilnadu, India

    R. Mathusoothana S. Kumar

Authors
  1. M. Leena Silvoster
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  2. R. Mathusoothana S. Kumar
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Correspondence to M. Leena Silvoster .

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Editors and Affiliations

  1. Department of Information Technology, Al Buraimi University College, Al Buraimi, Oman

    Mostafa Al-Emran

  2. Faculty of Engineering and IT, The British University in Dubai, Dubai, United Arab Emirates

    Khaled Shaalan

  3. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Leena Silvoster, M., Kumar, R.M.S. (2021). Segmentation of Images Using Watershed and MSER: A State-of-the-Art Review. In: Al-Emran, M., Shaalan, K., Hassanien, A. (eds) Recent Advances in Intelligent Systems and Smart Applications. Studies in Systems, Decision and Control, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-47411-9_25

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