GRAY-SCALE IMAGE PROCESSING FOR DIGITAL MAMMOGRAPHY

筛查性乳房X线摄影已被证明是一种有效的程序， 早期乳腺癌的诊断乳房X光检查发现的癌症往往 比乳房物理检查发现的更小， 考试较小和较低阶段的乳腺癌有更好的生存率 rates.不幸的是，大约10%的乳腺癌是不可见的 乳房X光检查，特别是在大量乳房的患者中， 腺组织如果我们能提高乳房X光检查的灵敏度 通过灰度操作使记录的信息可感知 技术（即“对比度增强”或“图像处理”）， 这些癌症更容易出现，早期发现可能会导致 大大降低了这一人群的死亡率。的目的 建议是评估不同的数据传输方法 在乳房X光片中记录到显示的强度，以增加 提高了乳腺X线摄影的灵敏度，便于早期识别乳腺 癌 本研究共分三个部分：第一，提供初步的 增强数字标准显示的灰度处理方法 临床现场的图像。这些最初的方法将来自结果 在预先设定的强度窗口上进行的实验， 开发具有适当人机工程学的软拷贝显示。二是 将把我们的图像显示处理方法应用于一系列有前景的 临床研究，以评估各种静态和动态 图像处理方法对检测异常的 从两个临床站点（MGDM和 TJUDM）。改进的图像处理算法和软拷贝人体工程学将 在整个资助期内， available.第三，我们将进行受控观察员研究，以确定 无论是使用有损压缩和解压缩的数字乳房X线照片， GE远程乳腺摄影组提供的压缩算法，包含 与原始图像相同的临床信息。

Screening mammography has proven to be an effective procedure in identifying early breast cancer. The cancers found by mammography tend to be smaller and of less advanced stages than those found by breast physical examination. Smaller and lower stage breast cancers have better survival rates. Unfortunately, approximately 10% of breast cancers are not visible with mammography, particularly in patients with large amounts of breast glandular tissue. If we can increase the sensitivity of mammography by making perceivable the recorded information through greyscale manipulation techniques (i.e. "contrast-enhancement" or "image processing") so that these cancers are more readily apparent, early detection may result in significantly reduced mortality in this population. The aim of this proposal is to evaluate different methods of transferring the data recorded in a mammogram to displayed intensities, in order to increase the sensitivity of mammography and facilitate earlier identification of breast cancer. The proposed research has three parts: first, we will provide initial greyscale processing methods to augment a standard display of the digital images at the clinical sites. These initial methods will come from results of experiments conducted at UNC on preset intensity windows, and development of softcopy display with appropriate ergonomics. Second, we will apply our image display processing methods to a series of prospective clinical studies to evaluate the affect of various static and dynamic image processing methods on the detection of abnormalities in the digitally acquired mammograms from the two clinical sites (MGDM and TJUDM). Improved image processing algorithms and softcopy ergonomics will be provided to the clinical sites throughout the grant period as they are available. Third, we will conduct a controlled observer study to determine whether a digital mammogram, compressed and decompressed using a lossy compression algorithms provided by the GE telemammography group, contains the same clinical information as the original image.