Journal of Healthcare Engineering / 2018 / Article / Alg 2

Research Article

Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Algorithm 2

Algorithm for LBP feature computation based on shape of mass in ROI as.
 Input: I(m, n); m = no. of rows and n = no. of column
 Output: LBP features
 Initialize: Radius R = 1 and neighborhood pixels P = 8
    Mask = [1 2 4 8 16 32 64 128 0]
    Sliding window coordinates: k = −1 : 1
    Count = 1 //number of pixels in I(m, n)
 for i = 1 to m do
    for j = 1 to n do
       //prepare local circular window
       I_local = I(i + k, j + k)
       center_pixel = I(i, j)
       //Arrange local neighborhoods of I(i, j) pixels in a row col = 1 : 9
       Lookup_table (i, col) = reshape(I_local [7, 17])
       //count number of pixels greater than zero
       a = length(find(Lookup_table > 0))
       //select pixel position from lookup-table for computation of LBP
       if a > 2       LBP_code(count,:) = I_local > center_pixel       count = count + 1
       end
    end
 end
 //compute histogram of LBP codes
 LBP_descriptor = LBP_descriptor/count
 //scale invariant

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