Lucier B J, Kallergi M, Qian W, DeVore R A, Clark R A, Saff E B, Clarke L P
Department of Mathematics, Purdue University, W. Lafayette, IN.
J Digit Imaging. 1994 Feb;7(1):27-38. doi: 10.1007/BF03168476.
An initial evaluation of Haar wavelets is presented in this study for the compression of mammographic images. Fifteen mammograms with 105 microns/pixel resolution and varying dynamic range (10 and 12 bits per pixel) containing clustered microcalcifications were compressed with two different rates. The quality and content of the compressed reconstructed images was evaluated by an expert mammographer. The visualization of the cluster was on the average good but degraded with increasing compression because of the discontinuities introduced by these types of wavelets as the compression rate increases. However, the artifacts in the decoded images were seen as totally artificial and were not misinterpreted by the radiologist as calcifications. The classification of the parenchymal densities did not change significantly but the morphology of the calcifications was increasingly distorted as the compression rate increased leading to lower estimates of the suspiciousness of the cluster and higher uncertainties in the diagnosis. The uncompressed and two sets of compressed images were also processed by a wavelet method to extract the calcifications. Despite the fact that the segmentation algorithm generated several false-positive signals in highly compressed images, all true clusters were successfully segmented indicating that the compression process preserved the features of interest. Our preliminary results indicated that wavelets could be used to achieve high compression rates of mammographic images without losing small details such as microcalcification clusters as well as detect the calcifications from either the uncompressed or compressed reconstructed data. Further research and application of multiresolution analysis to digital mammography is continuing.
本研究对哈尔小波在乳腺钼靶图像压缩方面进行了初步评估。对15幅分辨率为105微米/像素、动态范围各异（每像素10位和12位）且包含簇状微钙化的乳腺钼靶图像进行了两种不同压缩率的压缩。由专业乳腺放射科医生对压缩重建图像的质量和内容进行评估。簇状微钙化的可视化效果总体良好，但随着压缩率的增加而变差，这是因为这类小波在压缩率增加时会引入不连续性。然而，解码图像中的伪影被视为完全人为的，放射科医生不会将其误判为钙化。实质密度的分类没有显著变化，但随着压缩率的增加，钙化的形态越来越扭曲，导致对簇状微钙化可疑性的估计降低，诊断的不确定性增加。还采用一种小波方法对未压缩图像和两组压缩图像进行处理以提取钙化。尽管分割算法在高度压缩图像中产生了一些假阳性信号，但所有真实的簇状微钙化都成功地被分割出来，这表明压缩过程保留了感兴趣的特征。我们的初步结果表明，小波可用于实现乳腺钼靶图像的高压缩率，同时不丢失诸如微钙化簇等小细节，并且能够从未压缩或压缩重建数据中检测出钙化。多分辨率分析在数字乳腺钼靶方面的进一步研究和应用仍在继续。
