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

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

3. 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

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

5. 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)

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

7. 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

8. 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.

9. 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.

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

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

12. Implementation Using C Using Monash Image Library Synthetic Images
Natural Images

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

14. 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.

15. Thank You
Are there any questions or comments?