Transcript Interim Presentation

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:
1. Characteristic feature thresholding or clustering
2.
3.
(Feature Domain)
Boundary detection (Spatial Domain)
Region growing (Spatial Domain)
Clustering and Region Merging
Clustering
 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
We pick a K value to a apply to the image
depending on the analyser of the image.
K=2
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
Seeds
Merge pixels with similar grey level
values together
Region Merging
Noise Removal
noise
Looking at spatial information to decide
whether to merge a noise with the
current region.
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?