Veldkamp W J, Karssemeijer N
Department of Radiology, Radboud University Hospital, Nijmegen, The Netherlands.
IEEE Trans Med Imaging. 2000 Jul;19(7):731-8. doi: 10.1109/42.875197.
Equalizing image noise has been shown to be an important step in automatic detection of microcalcifications in digital mammograms. In this study, an accurate adaptive approach for noise equalization is presented and investigated. No additional information obtained from phantom recordings is involved in the method, which makes the approach robust and independent of film type and film development characteristics. Furthermore, it is possible to apply the method on direct digital mammograms as well. In this study, the adaptive approach is optimized by investigating a number of alternative approaches to estimate the image noise. The estimation of high-frequency noise as a function of the grayscale is improved by a new technique for dividing the grayscale in sample intervals and by using a model for additive high-frequency noise. It is shown that the adaptive noise equalization gives substantially better detection results than does a fixed noise equalization. A large database of 245 digitized mammograms with 341 clusters was used for evaluation of the method.
图像噪声均衡已被证明是数字乳腺X线摄影中微钙化自动检测的重要步骤。在本研究中，提出并研究了一种精确的自适应噪声均衡方法。该方法不涉及从体模记录中获取的额外信息，这使得该方法具有鲁棒性，且与胶片类型和胶片显影特性无关。此外，该方法也可以应用于直接数字化乳腺X线摄影。在本研究中，通过研究多种估计图像噪声的替代方法对自适应方法进行了优化。通过一种将灰度级划分为采样间隔的新技术以及使用加性高频噪声模型，改进了作为灰度级函数的高频噪声估计。结果表明，自适应噪声均衡比固定噪声均衡能给出更好的检测结果。使用一个包含245幅数字化乳腺X线摄影图像和341个微钙化簇的大型数据库对该方法进行评估。
