Journal of Healthcare Engineering / 2018 / Article / Alg 3

Research Article

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

Algorithm 3

Summary of proposed method for FP reduction in mammograms.
(1)Load input image (img1)
(2)Apply CLAHE algorithm and obtain enhanced image (img2)
(3)Process img1 and img2 and obtain enhanced image using procedure given in Algorithm 1
(4)Remove pectoral muscle using proposed approach (Section 3.1.2)
(5)Extract neighbourhood features for each pixel and apply SOM clustering
(6)Obtain clustered image and separate out the tumorous cluster
(7)Extract detected regions i.e., ROI's from clustered result
(8)Extract Sparse Curvelet Coefficients (Subband) up to 2 level from each ROI
(9)Extract Sparse LBP code for each subband and obtain a combined feature vector for each ROI
(10)Classify each ROI into tumorous and nontumorous class i.e., TP and FP respectively
(11)Map each TP region on original mammogram (img1)

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