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) | | (12) | end |
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