Chang D C, Wu W R
Department of Communication Engineering, National Chiao Tung University, Hsinchu, Taiwan, ROC.
IEEE Trans Med Imaging. 1998 Aug;17(4):518-31. doi: 10.1109/42.730397.
The adaptive contrast enhancement (ACE) algorithm, which uses contrast gains (CG's) to adjust the high-frequency components of images, is a well-known technique for medical image processing. Conventionally, the CG is either a constant or inversely proportional to the local standard deviation (ILSD). However, it is known that conventional approaches entail noise overenhancement and ringing artifacts. In this paper, we present a new ACE algorithm that eliminates these problems. First, a mathematical model for the LSD distribution is proposed by extending Hunt's image model. Then, the CG is formulated as a function of the LSD. The function, which is nonlinear, is determined by the transformation between the LSD histogram and a desired LSD distribution. Using our formulation, it can be shown that conventional ACE's use linear functions to compute the new CG's. It is the proposed nonlinear function that produces an adequate CG resulting in little noise overenhancement and fewer ringing artifacts. Finally, simulations using some X-ray images are provided to demonstrate the effectiveness of our new algorithm.
自适应对比度增强（ACE）算法利用对比度增益（CG）来调整图像的高频分量，是医学图像处理中一种广为人知的技术。传统上，CG要么是一个常数，要么与局部标准差（ILSD）成反比。然而，众所周知，传统方法会导致噪声过度增强和振铃伪影。在本文中，我们提出了一种新的ACE算法来消除这些问题。首先，通过扩展亨特图像模型提出了一个LSD分布的数学模型。然后，将CG表示为LSD的函数。该函数是非线性的，由LSD直方图与期望的LSD分布之间的变换确定。使用我们的公式可以证明，传统的ACE使用线性函数来计算新的CG。正是所提出的非线性函数产生了适当的CG，从而使噪声过度增强较少且振铃伪影较少。最后，提供了一些使用X射线图像的模拟来证明我们新算法的有效性。
