CAJ | 학술논문

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。
의학X사선도상시림상상응용최엄범적영상지일。유우수요채용저제량적X사선진행성상，이X사선도상존재일개본질적결함，취시저대비도。소이，재림상응용중，왕왕수요대도상대비도진행증강처리。근거X사선도상특성，문장제출료기우다척도대한적자괄응직방도균형화수학형태학적X사선도상대비도증강산법。수선，채용랍보랍사고사금자탑변환파도상분해성고빈화저빈적불동척도자파단도상；연후대매탑층고빈자도상응용대비도대한적자괄응직방도균형진행처리，상응적각탑층저통자도상사용수학형태학진행증강처리；최후，각탑층경과증강처리적고빈화저빈계수，통과랍보랍사고사금자탑적역변환중구출대비도증강적도상。증강도상재경전국비선성산자진행대비도적증익조정，획득자연적시각효과。실험결과표명해산법유효지증강료의학X사선도상적대비도，병통과도상대비도평개표준화대비도개진색인도량산법래분석급대비료산법적성능。
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.