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Examples (Card. MR). (From Carl Jaffe, Yale Univ.). (From Carl Jaffe, Yale Univ). Examples (Card. Echo). Examples (Spine X-rays). (From Rodney Long, NLM). Examples (Carpal bones). Examples (Dorsal Fin). (Collaboration with UTMB and T A
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1. Medical Image Databases Hemant D. Tagare
Dept. of Diagnostic Radiology
Dept. of Electrical Engineering
Yale University
3. Examples (Card. Echo)
4. Examples (Spine X-rays)
5. Examples (Carpal bones)
6. Examples (Dorsal Fin)
7. Why are they different? Image content and semantics:
Hard to describe by text; best described graphically.
Hard to analyze automatically.
Rich in geometry.
Image features:
Complex.
Evolve with the database.
Technically difficult to index.
Queries:
Wider range (Browsing Research).
Nave User.
Low tolerance for errors.
8. Image Semantics
9. Defining Semantics by Images
10. Basic Mechanism
11. Image Databases Database creation
Tools for defining images and semantics.
12. Key Idea
13. Key Idea (contd.) In many bio-medical images, most geometric information can be calculated in an axiomatic fashion from relatively little information.
14. Schema
15. Segmentation
16. Schema Editor
17. Database Organization
18. Retrieval By Similarity
19. Features, Similarities, Indexing Trees
20. Vectors-Metrics
21. Non-vector- Metric
22. Vectors-Non-metric
23. Cardiac Ultrasound Database
24. Contd.
25. Dolphin Database
26. Contd.
27. Interesting Open Problems Curse of Dimension
Indexing tree performance degrades with increasing
dimension (>= 10).
Browsing
Other modes of retrieval besides range and Nearest- neighbor.
User Feedback
28. The Curse of Dimension
29. Optimal Covering
30. Non-Uniform Data
31. Browsing
32. User Feedback
33. Acknowledgements