Image Segmentation A Hybrid Method Using Clustering & Region Growing Interim Presentation By Timothy Liao Supervisor: Dr Sid Ray

Overview Image segmentation Research Context Image Segmentation Methods Clustering and Region Merging Implementation Conclusion

Image Segmentation What is it? First step in image analysis Partitioning of an image into non-overlapping regions. What is it used for? Detection of cancerous cells from medical images Detection of Roads from satellite images

Research Context Many Image Segmentation techniques. These techniques only work on certain images. Hybrid method, combining clustering and region merging.

Image Segmentation Methods Most image segmentation methods can be placed in one of three classes: Characteristic feature thresholding or clustering (Feature Domain) Boundary detection (Spatial Domain) Region growing (Spatial Domain)

Clustering and Region Merging K-means Clustering ISODATA Fuzzy K-Means Region Merging Region Growing Split and Merge

K-Means Clustering K-means Clustering is Most common method used in unsupervised clustering. Prior knowledge of K is needed. Algorithm Select K different grey level values from pixels in an image. While K mean values != previous k mean value do assign each pixel that has the closest grey level value to the k mean value. work out the new mean values for each k. end

Clustering of Grey Level Images Original Image K=3 K=2 We pick a K value to a apply to the image depending on the analyser of the image.

Automatic Determination of K Ray and Turi’s automatic determination of K in colour image segmentation. (R.Tury, S.Ray 1998). Automatic determination of K for grey level images will be implemented in this research.

Region Merging Find Seed values within the image Merge pixels with similar grey level values together Seeds

Region Merging Noise Removal Looking at spatial information to decide whether to merge a noise with the current region. noise

Implementation Using C Using Monash Image Library Synthetic Images Natural Images

Conclusion Hybrid Image Segmentation technique should perform better than common techniques.

References R.H. Turi and S. Ray. K-means clustering for colour image segmentation with automatic detection of k. In Proceedings of Internation Conference on Sigmal and image Processing, pages 345–349, Las Vegas, Nevada, USA, 1998. M.R. Anderberg. Cluster Analysis for Application. New York: Academic Press, 1973. J.T Tou and R.C. Gonzalez. Pattern Recognition Principles. Addison-Wesley., Massachusetts, USA, 1974. E.W. Forgy. Cluster analysis of multivariate data: eciency vs. interpretability of classifications. abstract, Biometrics,, 21:768–769, 2000. J. MacQueen. Some methods for classification and analysis of multivariate observations. pages 281–279. Proceedings of Fifth Berkeley symposium on Mathematical Statistics and Probability, 1967. G. Coleman and H.C. Andrews. Image segmentation and clustering. pages 773–785. Proc, IEEE, 1979. R E. Woods R C. Gonzalez. Digital Image Processing. Addison-Wesley, 1992.

Thank You Are there any questions or comments?