基于多尺度带限的自适应直方图均衡和数学形态学的医学X射线图像对比度增强算法

Contrast Enhancement of Medical X-ray Images Based on Multiscale Limited Adaptive Histogram Equalization and Mathematical Morphology

医学X射线图像是临床上应用最广泛的影像之一。由于需要采用低剂量的X射线进行成像,而X射线图像存在一个本质的缺陷,就是低对比度。所以,在临床应用中,往往需要对图像对比度进行增强处理。根据X射线图像特性,文章提出了基于多尺度带限的自适应直方图均衡和数学形态学的X射线图像对比度增强算法。首先,采用拉普拉斯高斯金字塔变换把图像分解成高频和低频的不同尺度子波段图像;然后对每塔层高频子图像应用对比度带限的自适应直方图均衡进行处理,相应的各塔层低通子图像使用数学形态学进行增强处理;最后,各塔层经过增强处理的高频和低频系数,通过拉普拉斯高斯金字塔的逆变换重构出对比度增强的图像。增强图像再经全局非线性算子进行对比度的增益调整,获得自然的视觉效果。实验结果表明该算法有效地增强了医学X射线图像的对比度,并通过图像对比度评价标准和对比度改进索引度量算法来分析及对比了算法的性能。

The medical X-ray image is one of the images most widely applied in clinical applications. Because the low-dose X-ray image needed for imaging is of a low contrast, the X-ray image contrast enhancement is processed before the clinical application. A new algorithm for contrast enhancement of mammographic images was proposed in this paper. The approach was based on the multiscale transform and mathematical morphology. First of all, the Laplacian Gaussian pyramid operator was applied to transform the image into different scale sub-band images. In addition, the high-frequency sub-images were equalized by contrast limited adaptive histogram equalization and low-pass sub-images were processed by the mathematical morphology. Finally, the image of enhanced contrast was reconstructed from the Laplacian Gaussian pyramid coefifcients of high or low frequencies modiifed by contrast limited adaptive histogram equalization and mathematical morphology respectively. The enhanced image was processed by a global non-linear operator. The experimental results show that the proposed algorithm is effective for the contrast enhancement of the medical X-ray image. The performances of the proposed algorithm were measured by contrast evaluation criterion for image and contrast improvement index.