DOI QR코드

DOI QR Code

Efficient Image Segmentation Using Morphological Watershed Algorithm

형태학적 워터쉐드 알고리즘을 이용한 효율적인 영상분할

Kim, Young-Woo;Lim, Jae-Young;Lee, Won-Yeol;Kim, Se-Yun;Lim, Dong-Hoon
김영우;임재영;이원열;김세윤;임동훈

  • Published : 2009.08.31

Abstract

This paper discusses an efficient image segmentation using morphological watershed algorithm that is robust to noise. Morphological image segmentation consists of four steps: image simplification, computation of gradient image and watershed algorithm and region merging. Conventional watershed segmentation exhibits a serious weakness for over-segmentation of images. In this paper we present a morphological edge detection methods for detecting edges under noisy condition and apply our watershed algorithm to the resulting gradient images and merge regions using Kolmogorov-Smirnov test for eliminating irrelevant regions in the resulting segmented images. Experimental results are analyzed in both qualitative analysis through visual inspection and quantitative analysis with percentage error as well as computational time needed to segment images. The proposed algorithm can efficiently improve segmentation accuracy and significantly reduce the speed of computational time.

Keywords

Morphological watershed algorithm;image segmentation;morphological gradient image;region merging;Kolmogorov-Smirnov test

References

  1. Daniel, W. W. (1978). Applied Nonparametric Statistics, Houghton Mifflin, Boston
  2. Gonzalez, R. C. and Woods, R. E. (1993). Digital Image Processing, Addison-Wesley
  3. Jain, A. K. (1989). Fundamentals of Digital Image Processing, Prentice Hall
  4. Jung, C. R. and Scharcanski, J. (2005). Robust watershed segmentation using wavelets, Image and Vision Computing, 23, 661-669 https://doi.org/10.1016/j.imavis.2005.03.001
  5. Lim, D. H. (2006). Robust edge detection in noisy images, Computational Statistics & Data Analysis, 50, 803-812 https://doi.org/10.1016/j.csda.2004.10.005
  6. Lim, D. H. and Jang, S. J. (2002). Comparison of two-sample tests for edge detection in noisy images, Journal of the Royal Statistical Society D-The Statistician, 51, 21-30 https://doi.org/10.1111/1467-9884.00295
  7. Vincent, L. and Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 583-598 https://doi.org/10.1109/34.87344
  8. Wang, D. (1997). A multiscale gradient algorithm for image segmentation using watersheds, Pattern Recognition, 30, 2043-2052 https://doi.org/10.1016/S0031-3203(97)00015-0
  9. Zhao, Y., Gui, W., Chen, Z., Tang, J. and Li, L. (2005). Medical images edge detection based on mathematical morphology, Engineering in Medicine and Biology Society, 6492-6495