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An Optimized Feature Selection Method For Breast Cancer Diagnosis in Digital Mammogram using Multiresolution Representation |
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PP: 2921-2928 |
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Author(s) |
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Mohamed Meselhy Eltoukhy,
Ibrahima Faye,
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Abstract |
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| This paper introduces a method for feature extraction from multiresolution representations (wavelet,curvelet) for
classification of digital mammograms. The proposed method selects the features according to its capability to distinguish between
different classes. The method starts with both performing wavelet and curvelet transform over mammogram images. The resulting
coefficients of each image are used to construct a matrix. Each row in the matrix corresponds to an image.The most significant
features, in terms of capabilities of differentiating classes,are selected. The method uses threshold values to select the columns that
will maximize the difference between the different classes’representatives. The proposed method is applied to the mammographic
image analysis society (MIAS) dataset. The results calculated using 2x5-folds cross validation show that the proposed method is able
to find an appropriate feature set that lead to significant improvement in classification accuracy.The obtained results were satisfactory
and the performances of both wavelet and curvelet are presented and compared. |
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