Empirical Driven Automatic Detection of Lobulation Imaging Signs in Lung CT

Algorithm 10

Bending energy based lobulation detection.

Input: Training set , Testing set TE, the outputted ROIs of Algorithm (or 9)

Output: The locations of detected lobulation regions

Stage 1: Training

for  each image in

 Step : Preprocess the image;

 Step : Obtain the image patches by sliding window method;

 Step : Obtain the edges of image patch using canny algorithm;

 Step : Compute bending energy for each edge fragment in ;

 for  each edge fragment in

  step : Compute the curvature for all points of ;

  step : Compute the bending energy of edge point using curvature ;

  step : Compute the average bending energy of edge points locating in a curved line sliding window with the given size ;

 end

 Step : Sort the average bending energy of edge points by descend;

 Step : Select Top 6 largest average bending energy ;

 Step : Compute the average value BE of as the bending energy feature (BEFeature, image feature) in current curved line

 sliding window;

 Step : Determine Threshold Th₁ and Th₂ empirically based on the training samples, so that BEFeature Th1 and

 BEFeature Th2 for lobulation.

end

Stage 2: Lobulation detection

for  each image in testing set TE

 Step : Obtain the result ROIs (i.e., bounding box region) for current image from ;

 for  each ROI in R

  Step : Transform the gray value in to CT value by formula (1);

  Step : Compute the bending energy BE for using the similar steps in train stage;

  Step : lobulation labeling

   If  BE > Th₁ and BE < Th₂

    Label as lobulation;

   end

 end

end