Image Segmentation Using Automatic Seeded Region Growing and Instance-Based Learning

  • Octavio Gómez
  • Jesús A. González
  • Eduardo F. Morales
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


Segmentation through seeded region growing is widely used because it is fast, robust and free of tuning parameters. However, the seeded region growing algorithm requires an automatic seed generator, and has problems to label unconnected pixels (the unconnected pixel problem). This paper introduces a new automatic seeded region growing algorithm called ASRG-IB1 that performs the segmentation of color (RGB) and multispectral images. The seeds are automatically generated via histogram analysis; the histogram of each band is analyzed to obtain intervals of representative pixel values. An image pixel is considered a seed if its gray values for each band fall in some representative interval. After that, our new seeded region growing algorithm is applied to segment the image. This algorithm uses instance-based learning as distance criteria. Finally, according to the user needs, the regions are merged using ownership tables. The algorithm was tested on several leukemia medical images showing good results.


Image Segmentation Seeded Region Growing Instance-based learning Color image Multispectral image 


  1. 1.
    Adams, R., Bischof, L.: Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647 (1994)CrossRefGoogle Scholar
  2. 2.
    Ballard, D.H., Brown, C.M.: Computer Vision, 1st edn. Prentice-Hall, Boston Massachusetts (1982)Google Scholar
  3. 3.
    Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J.: Color image segmentation: advances and prospects. Pattern Recognition 34, 2259–2281 (2001)zbMATHCrossRefGoogle Scholar
  4. 4.
    Cheng, H.D., Jiang, X.H., Wang, J.: Color image segmentation based on homogram thresholding and region merging. Pattern Recognition 35, 373–393 (2002)zbMATHCrossRefGoogle Scholar
  5. 5.
    Dougherty, J., Kohavi, R., Sahami, M.: Supervised and Unsupervised Discretization of Continuous Features. Machine Learning. In: Proceedings of the Twelfth International Conference, vol. 12, pp. 194–202. Morgan Kaufmann, San Francisco (1995)Google Scholar
  6. 6.
    Fan, J., Zeng, G., Body, M., Hacid, M.: Seeded region growing: and extensive and comparative study. Pattern Recognition 26, 1139–1156 (2005)CrossRefGoogle Scholar
  7. 7.
    MVTec Software GmbH. Halcon: Machine vison software for business applications. MVTec Software GmbH, Munchen Germany (2007)Google Scholar
  8. 8.
    Jeon, B., Jung, Y., Sang, K.: Image segmentation by unsupervised sparse clustering. Pattern Recognition Letters 27, 1139–1156 (2005)Google Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. In: Proceedings 1st International Conference on Computer Vision. International Journal of Computer Vision, vol. 1, pp. 321–331. Springer-Verlag, Netherlands (1988)Google Scholar
  10. 10.
    Paglieroni, D.W.: Design considerations for image segmentation quality assessment measures. Pattern Recognition 37, 1607–1617 (2004)CrossRefGoogle Scholar
  11. 11.
    Pichel, J.C., Singh, D.E., Rivera, F.F.: Image segmentation based on merging sub-optimal segmentations. Pattern Recognition Letters 27, 1105–1116 (2006)CrossRefGoogle Scholar
  12. 12.
    Quiao, Y., Hu, Q., Qian, G., Luo, S., Nowinski, W.L.: Thresholding based on variance and intensity contrast. Pattern Recognition 40, 596–698 (2007)CrossRefGoogle Scholar
  13. 13.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1, 81–106 (1986)Google Scholar
  14. 14.
    Shih, F.Y., Cheng, S.: Automatic seeded region growing for color image segmentation. Image and Vision Computing 23, 877–886 (2005)CrossRefGoogle Scholar
  15. 15.
    Siebert, A.: Dynamic Region Growing. Vision Interface 97. Massachusetts Institute of Technology, Cambridge Massachusetts (1997)Google Scholar
  16. 16.
    Tremeau, A., Borel, N.: A region growing and merging algorithm to color image segmentation. Pattern Recognition 30, 1191–1203 (1997)CrossRefGoogle Scholar
  17. 17.
    Zouagui, T., Benoit-Cattin, H., Odet, C.: Image segmentation functional model. Pattern Recognition 37, 1785–1795 (2004)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Octavio Gómez
    • 1
  • Jesús A. González
    • 1
  • Eduardo F. Morales
    • 1
  1. 1.National Institute of Astrophisics, Optics and Electronics, Computer Science Department, Luis Enrique Erro Num 1, PueblaMéxico

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