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