The International Arab Journal of Information Technology (IAJIT)


Image Segmentation Based on Watershed and Edge Detection Techniques Nassir Salman

A combination of K-means, watershed segmentation method, and Difference In Strength (DIS) map was used to perform image segmentation and edge detection tasks. We obtained an initial segmentation based on K-means clustering technique. Starting from this, we used two techniques; the first is watershed technique with new merging procedures based on mean intensity value to segment the image regions and to detect their boundaries. The second is edge strength technique to obtain an accurate edge maps of our images without using watershed method. In this paper: We solved the problem of undesirable oversegmentation results produced by the watershed algorithm, when used directly with raw data images. Also, the edge maps we obtained have no broken lines on entire image and the final edge detection result is one closed boundary per actual region in the image.

[1] Alvarez L., Lion P. L., and Morel J. M., Image Selective Smoothing and Edge Detection by Non Linear Diffusion, SIAM Journal, vol. 29, no. 3, pp. 845-866, 1992.

[2] Chowdhury M. I. and Robinson J. A., Improving Image Segmentation Using Edge Information, in Proceedings of the 1stIEEE Conference on Electrical and Computer Engineering , Halifax, Canada, vol.1, pp. 312-316, 2000.

[3] Gary R. M. and Linde Y., Vector Quantizers and Predicative Quantizers for Gauss-Markov Sources, IEEE Transactions on Communication, vol. 30, no. 2, pp. 381-389, 1982.

[4] Salman N. and Liu C. Q., Image Segmentation and Edge Detection Based on Watershed Techniques, International Journal of Computers and Applications , vol. 25, no. 4, pp. 258-263, 2003.

[5] Tang H., Wu E. X., Ma Q. Y., Gallagher D., Perera G. M., and Zhuang T., MRI Brain Image Segmentation by Multi-Resolution Edge Detection and Region Selection, Computerized Medical Imaging and Graphics , vol. 24, no. 6, pp. 349-357, 2000.

[6] Thrasyvoulos N. P., An Adaptive Clustering Algorithm for Image Segmentation, IEEE Transaction on Signal Processing , vol. 40, no. 4, pp. 901-914, 1992.

[7] Tou J. T. and Gonzalez R. C., Pattern Recognition Principles , Addison Wesley, USA, pp. 75-97, 1974.

[8] Vincent L. and Soille P. Watershed in Digital Space: An Efficient Algorithm Based on Immersion Simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-593, 1991.

[9] Yan M. X. H. and Karp J. S., Segmentation of 3D Brain MR Using an Adaptive K-means Clustering Algorithm, in Proceedings of the 4th IEEE Conference on Nuclear Science Symposium and Medical Imaging , San Francisco, USA, vol.4., pp. 1529-1533, 1995.

[10] Yu Y. and Wang J., Image Segmentation Based on Region Growing and Edge Detection, in Proceedings of the 6th IEEE International Conference on Systems, Man and Cybernetics , Tokyo, vol.6., pp. 798-803, 1999. 110 The International Arab Journal of Information Technology, Vol. 3, No. 2, April 2006 Nassir Salman received his BSc, and MSc degrees from Mustansyriah University, Iraq, in 1983 and 1989 respectively, and his PhD degree in pattern recognition and intelligence systems, image processing engineering from Shanghai Jiao Tong University, China. Currently, he is a member of the Computer Science Department, Zarqa Private University, Jordan. His research interests include remote sensing, image processing and image analysis based on image segmentation, and edge detection techniques.