METHOD FOR DETERMINING OPTIMALLY WEIGHTED WAVELET TRANSFORM

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 (4, 6 and 8). Weights at individual scales of the wavelet transform are optimized based on a supervised learning method (6). In the learning method, an error function (12) represents a difference between a desired output (14) and a reconstructed image (10) 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.