Shenzhen Institute of Advanced Technology, Chinese Academy of Science, China ; Shenzhen Key Laboratory for Low-Cost Healthcare, China.
Comput Math Methods Med. 2013;2013:716948. doi: 10.1155/2013/716948. Epub 2013 Dec 12.
A new algorithm for feature and contrast enhancement of mammographic images is proposed in this paper. The approach bases on multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator is applied to transform the mammography into different scale subband images. In addition, the detail or high frequency subimages are equalized by contrast limited adaptive histogram equalization (CLAHE) and low-pass subimages are processed by mathematical morphology. Finally, the enhanced image of feature and contrast is reconstructed from the Laplacian Gaussian pyramid coefficients modified at one or more levels by contrast limited adaptive histogram equalization and mathematical morphology, respectively. The enhanced image is processed by global nonlinear operator. The experimental results show that the presented algorithm is effective for feature and contrast enhancement of mammogram. The performance evaluation of the proposed algorithm is measured by contrast evaluation criterion for image, signal-noise-ratio (SNR), and contrast improvement index (CII).
本文提出了一种新的乳腺图像特征增强和对比度增强算法。该方法基于多尺度变换和数学形态学。首先，应用拉普拉斯高斯金字塔算子将乳腺图像变换为不同尺度的子带图像。此外，通过对比度限制自适应直方图均衡化（CLAHE）对细节或高频子图像进行均衡化，对低通子图像进行数学形态学处理。最后，通过对比度限制自适应直方图均衡化和数学形态学分别对一个或多个层次的拉普拉斯高斯金字塔系数进行修改，从修改后的系数中重建增强的特征和对比度图像。增强后的图像通过全局非线性算子进行处理。实验结果表明，该算法对乳腺图像的特征增强和对比度增强是有效的。通过图像对比度评价准则、信噪比（SNR）和对比度改善指数（CII）来衡量所提出算法的性能评估。
