FAPBED Checkpoint Presentation: Feature Identification

1. FAPBEDCheckpoint Presentation:Feature Identification Danilo Scepanovic Josh Kirshtein Mentor: Ameet Jain

2. Sample Image

3. Difficult Surface To Detect • Faint Edges • Edges In Close Proximity • Relevance To Larger Problem Of Segmentation

4. Identified Properties • Pixel Density Value • Linear Gradient • Maximum 2D Gradient and Directionality • Pixel Disparity Magnification / Intensification More Properties to Analyze • Principle Component Analysis • Weighted Incidence Angles

5. Methods • Linear Gradient • Thresholding • Close Proximity Edge Enhancement • 2D Gradient • Intensification

6. Linear Gradient • Look at gradients along X and Y direction independently • Detect edges by observing: • Raw pixel values • Gradient values along single axis • Range of gradient values along single axis • Future: Weight by normal to surface as detected by 2D gradient analysis

7. Y = 285 Raw Pixel Value Gradient Value Range of Gradient

8. X = 215 Raw Pixel Value Gradient Value Range of Gradient

9. Thresholding • Densities are systematically distributed within a slice and a volume • Thresholding separates main classes Pixel Densities from Original Slices Derivative of Pixel Densities

10. Play Threshold Movie Thresholding Characteristics • Notice loss of soft tissue occurs between 50-70 • Insides of bones disappear between 70-80 • Above that, bone edges disapear

11. Close Proximity Edge Enhancer • Apply a filter that will enhance gaps between bones in close proximity • Involves looking at some number of neighbors and adjusting pixel values • Good at reducing pixel values that lie between bones (max pixel values unchanged) • Future: Use to enhance detection at bone junctions

13. How do we get more information from the image?

14. 2D Gradient • Convolve image with 2D gradient detector: • Maximal gradient • Direction of max gradient • Results: Enhances all edges in image • Future: Use to enhance confidence in a detected edge and to perform PCA and/or Weighted Incidence Angle analysis

15. First 2D Gradient Filter • Compute gradient across entire diameter of box (8 directions) • Pick max value • Determine direction Window Size = 3 Play Edge Movie

16. Window Size = 3

17. Window Size = 5

18. Window Size = 7

19. Arrows Indicate Direction of Maximum Gradient

20. Second 2D Gradient Filter • Compute gradient originating from center of box (8 directions) • Pick max value • Determine direction Window Size = 5

21. Window Size = 3

22. Window Size = 5

23. Window Size = 7

24. Comparison of both methods

25. Method 1 (Window = 3)

26. Method 2 (Window = 3)

27. Difference

28. Intensifier • Increase pixel densities that lie above the local mean • Decrease pixel densities that lie below the local mean Play Intensifier Movies

29. Intensifier Movies • As average box size increases, edges become thicker while soft tissue noise is suppressed • Smaller box size correlates with larger speckle and image obfuscation • Optimal clarity is achieved after first few feedback-loop iterations • Forcing hard classification introduces significant noise and results in information loss • Increasing box size yields thicker edges • Compounding final images from different box sizes yields more information

30. Timeline

31. Hurdles • Difficulties • Finding properties of surfaces • Combining different results into coherent image • Starting to implement methods • Dependencies Not Met • None

32. Thanks to: Ameet Jain Ofri Sadowski Dr Russell Taylor Mathworks