论文标题
未完成的2D小波谱及其在乳腺癌诊断中的使用
Non-decimated 2D Wavelet Spectrum and Its Use in Breast Cancer Diagnostics
论文作者
论文摘要
为了提高乳腺癌检测的诊断准确性，一些研究人员使用了基于小波的工具，这些工具为有助于诊断决策提供了更多的见解和信息。但是，可以提高此类诊断的准确性。本文介绍了基于小波的技术，基于非数量的小波变换（NDWT）的缩放估计，从而改善了比缩放参数估计，而不是传统方法。 NDWT的一个独特特征是，它不会在多尺度上脱离小波系数，从而导致冗余输出，这些输出用于降低缩放估计量的方差。该方法的另一个有趣的特征是输入的二元约束自由，这是基于标准小波的方法的典型特征。为了将NDWT方法的估计性能与基于常规的正交小波变换方法的估计性能进行比较，我们使用仿真来估算二维分数布朗尼字段中的Hurst指数。模拟的结果表明，所提出的方法改善了缩放率的常规估计器，并产生了均值较小的误差的估计器。我们将NDWT方法应用于将乳房X线照片分类为癌症或对照组，对于南佛罗里达大学数据库的公开乳房X线照片图像，请找到超过80％的诊断准确性。
To improve diagnostic accuracy of breast cancer detection, several researchers have used the wavelet-based tools, which provide additional insight and information for aiding diagnostic decisions. The accuracy of such diagnoses, however, can be improved. This paper introduces a wavelet-based technique, non-decimated wavelet transform (NDWT)-based scaling estimation, that improves scaling parameter estimation over the traditional methods. One distinctive feature of NDWT is that it does not decimate wavelet coefficients at multiscale levels resulting in redundant outputs which are used to lower the variance of scaling estimators. Another interesting feature of the proposed methodology is the freedom of dyadic constraints for inputs, typical for standard wavelet-based approaches. To compare the estimation performance of the NDWT method to a conventional orthogonal wavelet transform-based method, we use simulation to estimate the Hurst exponent in two-dimensional fractional Brownian fields. The results of the simulation show that the proposed method improves the conventional estimators of scaling and yields estimators with smaller mean-squared errors. We apply the NDWT method to classification of mammograms as cancer or control and, for publicly available mammogram images from the database at the University of South Florida, find the the diagnostic accuracy in excess of 80%.
