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Soft Computing for Sustainability Science pp 235–258Cite as

An Updated Review on Watershed Algorithms

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 358))

Abstract

Watershed identification is one of the main areas of study in the field of topography. It is critical in countless applications including sustainability and flood risk evaluation. Beyond its original conception, the watershed algorithm has proved to be a very useful and powerful tool in many different applications beside topography, such as image segmentation. Although there are a few publications reviewing the state-of-the-art of watershed algorithms, they are now outdated. In this chapter we review the most important works done on watershed algorithms, including the problem over-segmentation and parallel approaches. Open problems and future work are also investigated.

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References

  1. Ancin, H., Roysam, B., Dufresne, T., Chestnut, M., Ridder, G., Szarowski, D., Turner, J.: Advances in automated 3-d image analysis of cell populations imaged by confocal microscopy. Cytometry 25(3), 221–234 (1996)

    Article  Google Scholar 

  2. Andreatta, S., Wallinger, M., Posch, T., Psenner, R.: Detection of subgroups from flow cytometry measurements of heterotrophic bacterioplankton by image analysis. Cytometry 44(3), 218–225 (2001)

    Article  Google Scholar 

  3. Angel Viji, K., Jayakumari, J.: Performance evaluation of standard image segmentation methods and clustering algorithms for segmentation of mri brain tumor images. Euro. J. Sci. Res. 79(2), 166–179 (2012)

    Google Scholar 

  4. Arslan, S., Ozyurek, E., Gunduz-Demir, C.: A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images. Cytometry Part A 85(6), 480–490 (2014)

    Article  Google Scholar 

  5. Barriuso, P.Q., Heras, D., Argüello, F.: Efficient gpu asynchronous implementation of a watershed algorithm based on cellular automata, pp. 79–86, (2012)

    Google Scholar 

  6. Becattini, G., Mattos, L., Caldwell, D.: A novel framework for automated targeting of unstained living cells in bright field microscopy. pp. 195–198 (2011)

    Google Scholar 

  7. Beucher, S., Bilodeau, M.: Road segmentation and obstacle detection by a fast watershed transformation. pp. 296–301 (1994)

    Google Scholar 

  8. Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. workshop published (1979)

    Google Scholar 

  9. Beucher, S., Meyer, F.: The morphological approach to segmentation: the watershed transformation. mathematical morphology in image processing. Opt. Eng. 34, 433–481 (1993)

    Google Scholar 

  10. Bieniecki, W.: Oversegmentation avoidance in watershed-based algorithms for color images. pp. 169–172 (2004)

    Google Scholar 

  11. Bieniek, A., Moga, A.: An efficient watershed algorithm based on connected components. Pattern Recognit. 33(6), 907–916 (2000)

    Article  Google Scholar 

  12. Bullet, J., Gaujoux, T., Borderie, V., Bloch, I., Laroche, L.: A reproducible automated segmentation algorithm for corneal epithelium cell images from in vivo laser scanning confocal microscopy. Acta Ophthalmol. 92(4), e312–e316 (2014)

    Article  Google Scholar 

  13. Camilus, K., Govindan, V., Sathidevi, P.: Pectoral muscle identification in mammograms. J. Appl. Clin. Med. Phys. 12(3), 215–230 (2011)

    Article  Google Scholar 

  14. Chen, L.-C., Nguyen, T.-H., Lin, S.-T.: Viewpoint-independent 3d object segmentation for randomly stacked objects using optical object detection. Meas. Sci. Technol. 26(10) (2015)

    Google Scholar 

  15. Chen, T.: Gushing and immersion alternative watershed algorithm, pp. 246–248 (2001)

    Google Scholar 

  16. Cheng, J.-Z., Chen, K.-W., Chou, Y.-H., Chen, C.-M.: Cell-Based Image Partition and Edge Grouping: A Nearly Automatic Ultrasound Image Segmentation Algorithm for Breast Cancer Computer Aided Diagnosis. vol. 6915 (2008)

    Google Scholar 

  17. Chien, S.-Y., Chen, L.-G.: Reconfigurable morphological image processing accelerator for video object segmentation. J. Signal Process. Syst. 62(1), 77–96 (2011)

    Article  Google Scholar 

  18. Christ, M.., Parvathi, R.: Segmentation of medical image using clustering and watershed algorithms. Am. J. Appl. Sci. 8(12), 1349–1352 (2011)

    Google Scholar 

  19. Chung, K.-L., Lai, Y.-S., Huang, P.-L.: An efficient predictive watershed video segmentation algorithm using motion vectors. J. Inf. Sci. Eng. 26(2), 699–711 (2010)

    Google Scholar 

  20. Cousty, J., Bertrand, G., Najman, L., Couprie, M.: Watershed cuts: Minimum spanning forests and the drop of water principle. IEEE Trans. Pattern Anal. Mach. Intell. 31(8), 1362–1374 (2009)

    Article  Google Scholar 

  21. De Smet, P., Pires, R.L.V.: Implementation and analysis of an optimized rainfalling watershed algorithm. SPIE Int. Soc. Opt. Eng. 3974, 759–766 (2000)

    Google Scholar 

  22. Digabel, H., Lantuejoul, C.: Iterative algorithms. pp. 85–89 (1978)

    Google Scholar 

  23. Dubey, R., Hanmandlu, M., Gupta, S.: A comparison of two methods for the segmentation of masses in the digital mammograms. Comput. Med. Imaging Graph. 34(3), 185–191 (2010)

    Article  Google Scholar 

  24. Elsalamony, H.: Detecting distorted and benign blood cells using the hough transform based on neural networks and decision trees. In: Deligiannidis, L., Arabnia, H. (eds.) Emerging Trends in Image Processing, Computer Vision and Pattern Recognition, pp. 457–473 (2014)

    Google Scholar 

  25. Gao, H., Xue, P., Lin, W.: A new marker-based watershed algorithm. vol. 2, pp. II81–II84 (2004)

    Google Scholar 

  26. Gao, L., Yang, S., Xia, J., Liang, J., Qin, Y.: A new marker-based watershed algorithm (2007)

    Google Scholar 

  27. Gies, V., Bernard, T.: Statistical solution to watershed over-segmentation. Int. Conf. Image Process. 3, 1863–1866 (2004)

    Google Scholar 

  28. Guo, Z., Xin, Y., Liu, S., Lv, X., Li, S.: Comparisons of fat quantification methods based on mri segmentation, pp. 1817–1821 (2014)

    Google Scholar 

  29. Hagyard, D., Razaz, M., Atkin, P.: Analysis of watershed algorithms for greyscale images. Proc. 3rd IEEE Int. Conf. Image Process. 3, 41–44 (1996)

    Article  Google Scholar 

  30. Held, C., Wenzel, J., Webel, R., Marschall, M., Lang, R., Palmisano, R., Wittenberg, T.: Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology. In: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, pp. 6487–6490 (2011)

    Google Scholar 

  31. Hucko, M., Srámek, M.: Streamed watershed transform on gpu for processing of large volume data, pp. 137–141 (2012)

    Google Scholar 

  32. Idbraim, S., Mammass, D., Aboutajdine, D., Ducrot, D.: An automatic system for urban road extraction from satellite and aerial images. WSEAS Trans. Signal Process. 4(10), 563–572 (2008)

    Google Scholar 

  33. Ikedo, Y., Fukuoka, D., Hara, T., Fujita, H., Takada, E., Endo, T., Morita, T.: Development of a fully automatic scheme for detection of masses in whole breast ultrasound images. Med. Phys. 34(11), 4378–4388 (2007)

    Article  Google Scholar 

  34. Jabid, T., Mohammad, T., Ahsan, T., Abdullah-Al-Wadud, M., Chae, O.: An edge-texture based moving object detection for video content based application. pp. 112–116 (2011)

    Google Scholar 

  35. Jiafu, L., Yan, L., Wenfeng, Z., Jing, L.: Storm floods risk assessments by ga-bp: a case study of seven countries in Asia. Int. J. Adv. Comput. Technol. 3(10), 323–329 (2011)

    Google Scholar 

  36. Jianhua, L., Shuang, W., Licheng, J.: Method to reduce over-segmentation of images using immune clonal algorithm. vol. 6786 (2007)

    Google Scholar 

  37. Jouini, M., Vega, S., Mokhtar, E.: Multiscale characterization of pore spaces using multifractals analysis of scanning electronic microscopy images of carbonates. Nonlinear Process. Geophys. 18(6), 941–953 (2011)

    Article  Google Scholar 

  38. JW, L., JA, D.: Optimal identification of lumped watershed models. Water Resour. Res. 5(3), 583–590 (1969)

    Article  Google Scholar 

  39. Kauffmann, C., Piche, N.: A cellular automaton for ultra-fast watershed transform on gpu (2008)

    Google Scholar 

  40. Kekre, H., Sarode, T., Gharge, S.: Vector quantization for tumor demarcation of mammograms. Commun. Comput. Inf. Sci. 70, 157–163 (2010)

    Google Scholar 

  41. Kollorz, E., Angelopoulou, E., Beck, M., Schmidt, D., Kuwert, T.: Using power watersheds to segment benign thyroid nodules in ultrasound image data, pp. 124–128 (2011)

    Google Scholar 

  42. Kong, J., Cooper, L., Kurc, T., Brat, D., Saltz, J.: Towards building computerized image analysis framework for nucleus discrimination in microscopy images of diffuse glioma, pp. 6605–6608 (2011)

    Google Scholar 

  43. Körbes, A., Lotufo, R.: Analysis of the watershed algorithms based on the breadth-first and depth-first exploring methods. pp. 133–140 (2009)

    Google Scholar 

  44. Körbes, A., Vitor, G., De Alencar Lotufo, R., Ferreira, J.: Advances on watershed processing on gpu architecture. In: Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence. Lecture Notes in Bioinformatics, vol. 6671, pp. 260–271, LNCS (2011)

    Google Scholar 

  45. Linguraru, M., Howe, R.: Texture-based instrument segmentation in 3d ultrasound images, vol. 6144 II (2006)

    Google Scholar 

  46. Liu, J., Chen, K.: Novel method of mri medical image segmentation combining watershed algorithm and wkfcm algorithm. Appl. Mech. Mater. 121–126, 4518–4522 (2012)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  48. Mahmoudi, R., Akil, M.: Real time topological image smoothing on shared memory parallel machines, vol. 7871 (2011)

    Google Scholar 

  49. Malpica, N., De Solórzano, C., Vaquero, J., Santos, A., Vallcorba, I., García-Sagredo, J., Del Pozo, F.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 28(4), 289–297 (1997)

    Article  Google Scholar 

  50. Mei, T., Li, D., Qin, Q.: Application of knowledge based watershed transform approach to road detection, vol. 6045 II (2005)

    Google Scholar 

  51. 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 

  52. Mendonca, A.S., Rezende, R.A.: Application of geographical information systems and stochastic hydrology to the siting and design of water reservoirs. In: International Geoscience and Remote Sensing Symposium. IGARSS’99, vol. 2, pp. 1220–1222 (1999)

    Google Scholar 

  53. Meyer, F.: Topographic distance and watershed lines. Signal Process. 38(1), 113–125 (1994)

    Article  MATH  Google Scholar 

  54. Moga, A., Cramariuc, B., Gabbouj, M.: Parallel watershed transformation algorithms for image segmentation. Parallel Comput. 24(14), 1981–2001 (1998)

    Article  Google Scholar 

  55. Moga, A., Gabbouj, M.: Parallel marker-based image segmentation with watershed transformation. J. Parallel Distrib. Comput. 51(1), 27–45 (1998)

    Article  MATH  Google Scholar 

  56. Mohan, E., Sugumaran, R., Venkatachalam, K.: Automatic brain and tumor segmentation in mri using fuzzy classification with integrated bayesian model. Int. J. Appl. Eng. Res. 9(24), 25859–25870 (2014)

    Google Scholar 

  57. Mortensen, E.N., Barrett, W.A.: Toboggan-based intelligent scissors with a four-parameter edge model. In: CVPR, IEEE Computer Society, pp. 2452–2458 (1999)

    Google Scholar 

  58. Moumoun, L., El Far, M., Chahhou, M., Gadi, T., Benslimane, R.: Solving the 3d watershed over-segmentation problem using the generic adjacency graph (2010)

    Google Scholar 

  59. Muzylev, E., Uspensky, A.: Modelling the Hydrological Cycle of River Basins Using High Resolution Satellite Information, pp. 241–248. IAHS-AISH Publication, Wallingford (2001)

    Google Scholar 

  60. Najman, L., Couprie, M.: Watershed algorithms and contrast preservation. In: Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 2886, pp. 62–71 (2003)

    Google Scholar 

  61. Nithya, A., Kayalvizhi, R.: Extended fuzzy switching median filter and morphological algorithm for medical image segmentation. ARPN J. Eng. Appl. Sci. 10(1), 80–90 (2015)

    Google Scholar 

  62. Noguet, D.: Massively parallel implementation of the watershed based on cellular automata, pp. 42–52 (1997)

    Google Scholar 

  63. Osma-Ruiz, V., Godino-Llorente, J., Sáenz-Lechón, N., Gómez-Vilda, P.: An improved watershed algorithm based on efficient computation of shortest paths. Pattern Recognit. 40(3), 1078–1090 (2007)

    Article  MATH  Google Scholar 

  64. Patino, L.: Fuzzy relations applied to minimize over segmentation in watershed algorithms. Pattern Recognit. Lett. 26(6), 819–828 (2005)

    Article  Google Scholar 

  65. Peng, B., Xu, A., Li, H., Han, Y.: Road extraction based on object-oriented from high-resolution remote sensing images (2011)

    Google Scholar 

  66. Perry, E., Norton, S., Kamman, N., Lorey, P., Driscoll, C.: Deconstruction of historic mercury accumulation in lake sediments, Northeastern United States. Ecotoxicology 14(1–2), 85–99 (2005)

    Article  Google Scholar 

  67. Plaza, A., Plaza, J., Valencia, D., Martinez, P.: Parallel segmentation of multi-channel images using multi-dimensional mathematical morphology (2008)

    Google Scholar 

  68. Procházka, A., Vysata, O., Jerhotova, E.: Wavelet use for reduction of watershed transform over-segmentation in biomedical images processing (2010)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  71. Randhir, T., Lee, J., Engel, B.: Multiple criteria dynamic spatial optimization to manage water quality on a watershed scale. Trans. Am. Soc. Agric. Eng. 43(2), 291–299 (2000)

    Article  Google Scholar 

  72. Roerdink, J., Meijster, A.: The watershed transform: definitions, algorithms and parallelization strategies. Fundamenta Informaticae 41(1–2), 187–228 (2000)

    MathSciNet  MATH  Google Scholar 

  73. Rong, J., Pan, Y.-L.: Accuracy improvement of graph-cut image segmentation by using watershed. Adv. Mater. Res. 341–342, 546–549 (2012)

    Google Scholar 

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

    Google Scholar 

  75. Sheshadri, H., Kandaswamy, A.: Application of watershed algorithms to mammogram image analysis. IETE Tech. Rev. 23(3), 173–178 (2006)

    Article  Google Scholar 

  76. Shrimali, V., Anand, R., Kumar, V.: Current trends in segmentation of medical ultrasound b-mode images: a review. IETE Tech. Rev. 26(1), 8–17 (2009)

    Article  Google Scholar 

  77. Sinha, K., Sinha, G.: Efficient segmentation methods for tumor detection in mri images (2014)

    Google Scholar 

  78. Smistad, E., Falch, T., Bozorgi, M., Elster, A., Lindseth, F.: Medical image segmentation on gpus - a comprehensive review. Med. Image Anal. 20(1), 1–18 (2015)

    Article  Google Scholar 

  79. Sridhar, B., Reddy, K., Prasad, A.: An artificial neural network and mathematical morphology based computer aided detection system for cancer detection in mammograms. Int. J. Appl. Eng. Res. 9(23), 21079–21097 (2014)

    Google Scholar 

  80. Su, H., Yang, Z.-L., Dickinson, R., Wilson, C., Niu, G.-Y.: Multisensor snow data assimilation at the continental scale: the value of gravity recovery and climate experiment terrestrial water storage information. J. Geophys. Res. Atmospheres 115(10) (2010)

    Google Scholar 

  81. Sun, H., Yang, J., Ren, M.: A fast watershed algorithm based on chain code and its application in image segmentation. Pattern Recognit. Lett. 26(9), 1266–1274 (2005)

    Article  Google Scholar 

  82. Świercz, M., Iwanowski, M.: Fast, parallel watershed algorithm based on path tracing. In: Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 6375, pp. 317–324, LNCS(PART 2) (2010)

    Google Scholar 

  83. Swiercz, M., Iwanowski, M.: Waterball-iterative watershed algorithm with reduced oversegmentation. Adv. Intell. Soft Comput. 95(4), 385–394 (2011)

    Google Scholar 

  84. Tolosa, S., Blacher, S., Denis, A., Marajofsky, A., Pirard, J.-P., Gommes, C.: Two methods of random seed generation to avoid over-segmentation with stochastic watershed: application to nuclear fuel micrographs. J. Microsc. 236(1), 79–86 (2009)

    Article  MathSciNet  Google Scholar 

  85. Tonti, S., Di Cataldo, S., Bottino, A., Ficarra, E.: An automated approach to the segmentation of hep-2 cells for the indirect immunofluorescence ana test. Comput. Med. Imaging Graph. 40, 62–69 (2015)

    Article  Google Scholar 

  86. Tung, C.-P.: Climate change impacts on water resources of the tsengwen creek watershed in Taiwan. J. Am. Water Resour. Assoc. 37(1), 167–176 (2001)

    Article  Google Scholar 

  87. Uchida, S.: Image processing and recognition for biological images. Dev. Growth Differ. 55(4), 523–549 (2013)

    Article  Google Scholar 

  88. Van Neerbos, J., Najman, L., Wilkinson, M.: Towards a parallel topological watershed: First results. In: Lecture Notes in Computer Science Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 6671, pp. 248–259, LNCS (2011)

    Google Scholar 

  89. Vibha, L., Harshavardhan, G., Pranaw, K., Shenoy, P., Venugopal, K., Patnaik, L.: Lesion detection using segmentation and classification of mammograms, pp. 311–316(2007)

    Google Scholar 

  90. 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 

  91. Wagner, B., Dinges, A., Müller, P., Haase, G.: Parallel volume image segmentation with watershed transformation. In: Lecture Notes in Computer Science. Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 5575, pp. 420–429, LNCS (2009)

    Google Scholar 

  92. Wang, W., Shi, H., Wang, A.: Analysis on the future runoff changes in shiyang river basin based on genetic algorithm models (2012)

    Google Scholar 

  93. Wu, S., Hu, Y.: Parallelization research watershed algorithm 2012, 1524–1527 (2012)

    Google Scholar 

  94. Xu, G., Zhang, D., Liu, X.: Road extraction in high resolution images from google earth (2009)

    Google Scholar 

  95. Yang, F., Li, J., Xu, S.-H., Pan, G.-F.: The research of video segmentation algorithm based on image fusion in the wavelet domain, vol. 7659 (2010)

    Google Scholar 

  96. Yu, P.-Y., Zhang, G.-P., Yan, J.-W., Liu, M.-S.: The application of the watershed algorithm based on line-encoded in lung ct image segmentation (2011)

    Google Scholar 

  97. Zhang, X., Chen, L., Pan, L., Xiong, L.: Study on the image segmentation based on ica and watershed algorithm, pp. 505–508 (2012)

    Google Scholar 

  98. Zhang, X., Cheng, Y., Qian, Y., Zhuang, X.: Automatic video object segmentation based on spatio-temporal information, pp. 5314–5317 (2011)

    Google Scholar 

  99. Zhu, H., Zhang, B., Song, A., Zhang, W.: An improved method to reduce over-segmentation of watershed transformation and its application in the contour extraction of brain image, pp. 407–412 (2009)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    R. Romero-Zaliz

  2. Department of Architectonic and Engineering Graphic Expression, University of Granada, Granada, Spain

    J.F. Reinoso-Gordo

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    Carlos Cruz Corona

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Romero-Zaliz, R., Reinoso-Gordo, J. (2018). An Updated Review on Watershed Algorithms. In: Cruz Corona, C. (eds) Soft Computing for Sustainability Science. Studies in Fuzziness and Soft Computing, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-319-62359-7_12

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