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medical image databases

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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medical image databases

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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

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