基于Contourlet变换的医学图像增强

针对传统算法中存在噪声过增强的问题,给出了一种基于Contourlet变换的乳腺X线照片去噪增强算法。Contourlet变换作为一种多尺度几何分析方法,是一种具有多分辨的、局部化和方向化性质的图像表示方法。以此为基础,算法对图像分解后的Contourlet系数进行Stein阈值去噪,然后对不同子带上的各分解系数用非线性增益函数进行不同程度的增强。实验表明,该算法在去除噪声的同时有效突出了乳腺X线照片中的细微特征,有利于小乳腺癌的诊断。最后,文中还通过客观量化指标比较了不同增强算法的效果。

Image Enhancement Method Based on Contourlet Transform

A method aimed at minimizing image noise while optimizing contrast of mammographic image features is presented in this paper for more accurate detection of microcalcification clusters.The method is based on the contourlet transform,which is a multiresolution,local and directional image representation,and can effectively capture important features of images.The enhancement procedure consists of two steps: noise filtering by the Stein's thresholding and denoised contourlet coefficients modification via a nonlinear mapping function.The experimental results have shown an improved visualization of significant mammographic features by the proposed method.A comparison with other enhancement algorithms is also discussed by employing a measure named target to background contrast ratio using variance.