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Method for determining an optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms
Method for determining an optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms
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机译：基于监督训练的最佳加权小波变换确定方法，用于检测数字化乳腺X线照片中的微钙化
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摘要
A computer-aided diagnosis (CAD) method for detection of clustered microcalcifications in digital mammograms based on an image reconstruction using a substantially optimally weighted wavelet transform. Weights at individual scales of the wavelet transform are optimized based on a supervised learning method. In the learning method, an error function represents a difference between a desired output and a reconstructed image obtained from weighted wavelet coefficients of the wavelet transform for a given mammogram. The error function is then minimized by modifying the weights by means of a conjugate gradient algorithm. Performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis which indicated that the present invention outperformed both a difference- image technique and partial reconstruction method currently used in CAD methods.
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