FAPBED Checkpoint Presentation: Feature Identification Danilo Scepanovic Josh Kirshtein Mentor: Ameet Jain

Sample Image

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

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

Methods Linear Gradient Thresholding Close Proximity Edge Enhancement 2D Gradient Intensification

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

Y = 285 Raw Pixel Value Gradient Value Range of Gradient

Raw Pixel Value Gradient Value Range of Gradient X = 215

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

Play Threshold Movie Notice loss of soft tissue occurs between Insides of bones disappear between Above that, bone edges disapear Thresholding Characteristics

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

How do we get more information from the image?

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

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

Window Size = 3

Window Size = 5

Window Size = 7

Arrows Indicate Direction of Maximum Gradient

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

Window Size = 3

Window Size = 5

Window Size = 7

Comparison of both methods

Method 1 (Window = 3)

Method 2 (Window = 3)

Difference

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

Intensifier Movies 1)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 edges 4)Compounding final images from different box sizes yields more information

Timeline

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

Thanks to: Ameet Jain Ofri Sadowski Dr Russell Taylor Mathworks