Computer Engineering Department, Istanbul University, Avcilar, Istanbul, Turkey.
J Med Syst. 2010 Dec;34(6):993-1002. doi: 10.1007/s10916-009-9316-3. Epub 2009 Jun 6.
Breast cancer continues to be a significant public health problem in the world. The diagnosing mammography method is the most effective technology for early detection of the breast cancer. However, in some cases, it is difficult for radiologists to detect the typical diagnostic signs, such as masses and microcalcifications on the mammograms. This paper describes a new method for mammographic image enhancement and denoising based on wavelet transform and homomorphic filtering. The mammograms are acquired from the Faculty of Medicine of the University of Akdeniz and the University of Istanbul in Turkey. Firstly wavelet transform of the mammograms is obtained and the approximation coefficients are filtered by homomorphic filter. Then the detail coefficients of the wavelet associated with noise and edges are modeled by Gaussian and Laplacian variables, respectively. The considered coefficients are compressed and enhanced using these variables with a shrinkage function. Finally using a proposed adaptive thresholding the fine details of the mammograms are retained and the noise is suppressed. The preliminary results of our work indicate that this method provides much more visibility for the suspicious regions.
乳腺癌仍然是全球重大的公共卫生问题。诊断用乳腺 X 线摄影术是早期发现乳腺癌最有效的技术。然而，在某些情况下，放射科医生很难检测到乳腺 X 线片上的典型诊断迹象，如肿块和微钙化。本文描述了一种基于小波变换和同态滤波的乳腺图像增强和去噪新方法。乳腺 X 线片来自土耳其的阿克德尼兹大学医学院和伊斯坦布尔大学。首先对乳腺 X 线片进行小波变换，并用同态滤波器对逼近系数进行滤波。然后，用高斯和拉普拉斯变量分别对与噪声和边缘相关的小波细节系数进行建模。使用收缩函数对考虑的系数进行压缩和增强。最后，通过提出的自适应阈值，保留乳腺 X 线片的精细细节并抑制噪声。我们工作的初步结果表明，该方法为可疑区域提供了更高的可视性。
