Zhang W, Yoshida H, Nishikawa R M, Doi K
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, Illinois 60637, USA.
Med Phys. 1998 Jun;25(6):949-56. doi: 10.1118/1.598273.
We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.
我们正在开发一种用于检测数字化乳腺X线片中簇状微钙化的计算机辅助诊断（CAD）方案。我们之前已经报道过，在CAD方案的初始步骤中，使用经验选择的小波和尺度组合来检测微钙化。在本研究中，我们基于监督学习方法开发了一种技术，用于优化小波变换中各个尺度的权重，以提高我们CAD方案的性能。在学习过程中，制定了一个误差函数来表示期望输出与给定乳腺X线片加权小波系数重建图像之间的差异。然后通过共轭梯度算法修改小波系数的权重，使误差函数最小化。使用留一法对297个感兴趣区域（ROI）作为训练集对最小不对称Daubechies小波进行优化。通过使用上述ROI集，通过接收者操作特征（ROC）分析评估最佳加权小波的性能。分析得出ROC曲线下的平均面积为0.92，这优于我们现有CAD方案中使用的差异图像技术以及我们之前研究中使用的部分重建方法。
