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Enhancing Edge Detection in Medical Imaging: Methods and Challenges

This presentation explores advanced techniques in edge detection applied to challenging medical imaging surfaces. Key methods discussed include the utilization of linear and 2D gradient analysis, close proximity edge enhancement, and thresholding to identify faint edges and structures with high pixel density. The research highlights the significance of pixel disparity magnification and the role of principle component analysis in improving segmentation accuracy. Challenges faced during implementation and future directions, such as enhancing detection at bone junctions, are also addressed.

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Enhancing Edge Detection in Medical Imaging: Methods and Challenges

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  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

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

  13. 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

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

  15. Window Size = 3

  16. Window Size = 5

  17. Window Size = 7

  18. Arrows Indicate Direction of Maximum Gradient

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

  20. Window Size = 3

  21. Window Size = 5

  22. Window Size = 7

  23. Comparison of both methods

  24. Method 1 (Window = 3)

  25. Method 2 (Window = 3)

  26. Difference

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

  28. 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

  29. Timeline

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

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

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