1. Prostate Edge Detection Using a Knowledge Base
Supervisor
Prof. Magdy Salama By
Joseph Awad
November 18

2. Overview Introduction Algorithm Description Contrast Enhancement
Seed Point Localization
Knowledge Base Rules
Morphological Opening
Boundary Detection by Radial Scanning
Numerical Results
Conclusion

3. Introduction
Prostate cancer is diagnosed every 2.75 minutes, approximately 190,000 new cases each year.
It is the most commonly diagnosed cancer in America among men. More than 30,000 American men lose their lives to prostate cancer each year, one death every twenty minutes.
Prostate cancer incidence rates increased 192% between 1973 and 1992.

4. Algorithm Description

5. Contrast Enhancement Sticks Technique
For N * N neighbourhood in the image, there are 2N ‑ 2 short lines that pass through the central pixel, with N pixels in length. (3:2:17)

6. Seed Point Localization
The intensity level of the prostate is low with respect to its surrounding area.
The prostate is not in perimeter of the image but not necessarily in the medial.
20%, Disable black background.

7. Knowledge Base Rules Why ?

8. Knowledge Base Rules
The intensity level of prostate is less than the area around it.
Radial scanning of the image from seed point and removes any edge, which represents light to dark transitions.

9. Morphological Opening
There are still some false edges most of them have short lengths.
To remove these false edges, the short linked pixels with areas less than 50 pixels can be eliminated.

10. Boundary Detection by Radial Scanning
It might be still some false edges in the edge map. It is known that the prostate has a smooth curvature shape.
This algorithm analyses the obtained edge and filter out any pixels violate this knowledge. The final step in this algorithm is to use these edge pixels and interpolate the missing parts and found the best spline fit these pixels.

11. Numerical Results: Example 1

12. Numerical Results: Example 2

13. Numerical Results: Example 3

14. Numerical Results: Example 4

15. Numerical Results: Example 5

16. Conclusion The proposed algorithm is built on knowledge base.
It is fully automated.
It simulates an expert eyes.
The more knowledge base rules we apply, the better results we can get.

17. Thank you