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Fusion of Completed Local Binary Pattern Features with Curvelet Features for Mammogram Classification |
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PP: 3037-3048 |
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Author(s) |
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Syed Jamal Safdar Gardezi,
Ibrahima Faye,
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Abstract |
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| In this paper, fusion of texture features to improve classification accuracy by false positive reduction in mammograms
is proposed. The method uses texture features obtained from completed local binary pattern (CLBP) and grey level texture features
obtained from the Curvelet sub-bands. In the current experiments, mass and normal patches were obtained from Mammographic image
analysis Society (MIAS) and Image retrieval in medical applications (IRMA) datasets for mammograms. Texture features from both
methods are combined together to obtain the feature fusion matrix. Then Nearest neighbor classifier was used for classification to
evaluate the individual as well as enhanced features obtained from CLBP and curvelet. The classifier produces a classification accuracy
of 96.68% with 98.9% sensitivity and the false positive (FP) rates drop by 40% and 78% respectively for the enhanced features as
compared to the original results produced by both methods. The experimental results suggest that fusion of features improves the
performance of the system and is statistically significant. |
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