非抽取二维小波谱及其在乳腺癌诊断中的应用
Non-decimated 2D Wavelet Spectrum and Its Use in Breast Cancer Diagnostics
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，这些工具提供了额外的见解和信息，以帮助诊断决策。然而，这种诊断的准确性可以提高。本文介绍了一种基于小波变换的尺度估计方法--非抽取小波变换（NDWT）尺度估计方法，它改进了传统尺度参数估计方法。NDWT的一个显著特点是它不抽取小波系数在多尺度水平导致冗余输出，用于降低方差的缩放估计。所提出的方法的另一个有趣的特点是自由的二元约束的输入，典型的标准小波为基础的方法。为了比较传统的正交小波变换为基础的方法的NDWT方法的估计性能，我们使用模拟估计的赫斯特指数在二维分数布朗场。仿真结果表明，所提出的方法改进了传统的尺度估计，得到了具有较小均方误差的估计。我们应用NDWT方法将乳房X线照片分类为癌症或对照，并且对于来自南佛罗里达大学数据库的公开乳房X线照片图像，发现诊断准确率超过80%。
英文摘要：
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%.
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