fapbed checkpoint presentation: feature identification danilo scepanovic josh kirshtein mentor:...
TRANSCRIPT
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FAPBEDCheckpoint Presentation:
Feature Identification
Danilo ScepanovicJosh Kirshtein
Mentor: Ameet Jain
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Sample Image
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Difficult Surface
To Detect
•Faint Edges
•Edges In Close Proximity
•Relevance To Larger Problem Of Segmentation
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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
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Methods
• Linear Gradient
• Thresholding
• Close Proximity Edge Enhancement
• 2D Gradient
• Intensification
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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
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Y = 285
Raw Pixel Value
Gradient Value
Range of Gradient
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Raw Pixel Value
Gradient Value
Range of Gradient
X = 215
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Thresholding• Densities are systematically
distributed within a slice and a volume
• Thresholding separates main classesPixel Densities from Original
Slices
Derivative of
Pixel Densities
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Play Threshold Movie
• Notice loss of soft tissue occurs between 50-70
• Insides of bones disappear between 70-80
• Above that, bone edges disapear
Thresholding Characteristics
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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
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How do we get more information
from the image?
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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
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First 2D Gradient Filter
• Compute gradient across entire diameter of box (8 directions)
• Pick max value
• Determine direction
Window Size = 3
Play Edge Movie
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Window Size = 3
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Window Size = 5
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Window Size = 7
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Arrows Indicate Direction of
Maximum Gradient
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Second 2D Gradient Filter
• Compute gradient originating from center of box (8 directions)
• Pick max value
• Determine direction
Window Size = 5
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Window Size = 3
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Window Size = 5
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Window Size = 7
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Comparison of both methods
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Method 1 (Window = 3)
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Method 2 (Window = 3)
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Difference
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Intensifier
• Increase pixel densities that lie above the local mean
• Decrease pixel densities that lie below the local mean
Play Intensifier Movies
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Intensifier Movies1) As average box size increases, edges
become thicker while soft tissue noise is suppressed
2) 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
3) Increasing box size yields thicker edges4) Compounding final images from different
box sizes yields more information
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Timeline
Item Target Date StatusBackground Reading 27-Feb CompleteThresholding Algorithm Implementation 12-Mar CompleteNeural Network Attempted 26-Mar CompleteSpeed Ups 16-Apr In ProgressFinal Program Evaluation 23-Apr In ProgressFine Tune 30-Apr Awaits
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Hurdles
• Difficulties– Finding properties of surfaces– Combining different results into coherent image– Starting to implement methods
• Dependencies Not Met– None
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• Thanks to:Ameet Jain
Ofri Sadowski
Dr Russell Taylor
Mathworks