Cheong Kwang-Ho, Yoon Jai-Woong, Park Soah, Hwang Taejin, Kang Sei-Kwon, Koo Taeryool, Han Tae Jin, Kim Haeyoung, Lee Me Yeon, Kim Kyoung Ju, Bae Hoonsik
Department of Radiation Oncology, Hallym University College of Medicine, Seoul, Korea.
J Appl Clin Med Phys. 2018 Sep;19(5):398-406. doi: 10.1002/acm2.12411. Epub 2018 Jul 9.
The poor quality of megavoltage (MV) images from electronic portal imaging device (EPID) hinders visual verification of tumor targeting accuracy particularly during markerless tumor tracking. The aim of this study was to investigate the effect of a few representative image processing treatments on visual verification and detection capability of tumors under auto tracking.
Images of QC-3 quality phantom, a single patient's setup image, and cine images of two-lung cancer patients were acquired. Three image processing methods were individually employed to the same original images. For each deblurring, contrast enhancement, and denoising, a total variation deconvolution, contrast-limited adaptive histogram equalization (CLAHE), and median filter were adopted, respectively. To study the effect of image enhancement on tumor auto-detection, a tumor tracking algorithm was adopted in which the tumor position was determined as the minimum point of the mean of the sum of squared pixel differences (MSSD) between two images. The detectability and accuracy were compared.
Deblurring of a quality phantom image yielded sharper edges, while the contrast-enhanced image was more readable with improved structural differentiation. Meanwhile, the denoising operation resulted in noise reduction, however, at the cost of sharpness. Based on comparison of pixel value profiles, contrast enhancement outperformed others in image perception. During the tracking experiment, only contrast enhancement resulted in tumor detection in all images using our tracking algorithm. Deblurring failed to determine the target position in two frames out of a total of 75 images. For original and denoised set, target location was not determined for the same five images. Meanwhile, deblurred image showed increased detection accuracy compared with the original set. The denoised image resulted in decreased accuracy. In the case of contrast-improved set, the tracking accuracy was nearly maintained as that of the original image.
Considering the effect of each processing on tumor tracking and the visual perception in a limited time, contrast enhancement would be the first consideration to visually verify the tracking accuracy of tumors on MV EPID without sacrificing tumor detectability and detection accuracy.
电子射野影像装置（EPID）产生的兆伏级（MV）图像质量较差，这尤其在无标记肿瘤追踪过程中阻碍了对肿瘤靶向准确性的视觉验证。本研究的目的是调查几种具有代表性的图像处理方法对自动追踪下肿瘤视觉验证和检测能力的影响。
获取QC - 3质量模体的图像、一名患者的摆位图像以及两名肺癌患者的动态图像。对相同的原始图像分别采用三种图像处理方法。对于每种去模糊、对比度增强和去噪处理，分别采用全变差反卷积、对比度受限自适应直方图均衡化（CLAHE）和中值滤波器。为研究图像增强对肿瘤自动检测的影响，采用一种肿瘤追踪算法，其中肿瘤位置被确定为两幅图像之间像素差平方和均值（MSSD）的最小值点。比较了可检测性和准确性。
对质量模体图像进行去模糊处理后边缘更清晰，而对比度增强后的图像在结构区分度提高的情况下更易于阅读。同时，去噪操作降低了噪声，但以锐度为代价。基于像素值轮廓比较，对比度增强在图像感知方面优于其他方法。在追踪实验中，使用我们的追踪算法，只有对比度增强能在所有图像中检测到肿瘤。去模糊处理在总共75幅图像中有两帧未能确定目标位置。对于原始图像集和去噪后的图像集，有五幅相同的图像未能确定目标位置。同时，与原始图像集相比，去模糊后的图像检测准确性有所提高。去噪后的图像准确性降低。在对比度改善后的图像集情况下，追踪准确性与原始图像几乎保持一致。
考虑到每种处理对肿瘤追踪的影响以及在有限时间内的视觉感知，对比度增强将是在不牺牲肿瘤可检测性和检测准确性的前提下，视觉验证MV EPID上肿瘤追踪准确性的首要考虑因素。
