AUTOMATING STAGING OF COAL WORKERS PNEUMOCONIOSIS

煤工尘肺(CWP)，又称黑肺病， 标准胸部x光片显示圆形或不规则小阴影 由煤尘沉积在室内的生理反应引起的 阿龙。疾病的分期有两个参数：Profusion，一种衡量 不透明度浓度和不透明度大小/形状。在诊断X光时， 放射科医生在分期中有很大的变异性。 疾病。然而，一致的诊断是至关重要的，因为医学和 早期预防疾病进展等法律问题 检测，并对受影响的工人进行公平补偿。的目标是 本研究旨在开发一种最小化诊断变异性的方法。 通过利用数字化的计算机分析进行CWP的自动分级 胸部X光检查。这项工作是与国家 职业安全与健康研究所(西弗吉尼亚州摩根敦的NIOSH) 维护所有煤矿工人的X光片和相应的 美国的诊断和流行病学数据。 不存在解决模式问题的确定性方法论 识别，如CWP的自动分期。我们计划申请 利用导出的新特征的统计模式识别技术 来自数字化图像的数学建模。成对线性 将判别分析应用于降维和 基于最优特征子集的分类器的开发。 我们目前正在研究基于空间灰度级的要素 依赖矩阵、二维傅立叶谱、图像形态和 高斯-马尔可夫随机场。我们的分析将考察对 分类器开发中的特征的上下文(空间位置)， 以前没有被应用于这个问题。 培训和测试套片将从标准中获得 国际劳工组织的一套肺尘埃沉着病影片和 NIOSH电影，在匹兹堡大学数字化。诊断数据 关于电影的信息将从以前的读者(通常是两个)和 匹兹堡大学放射科。从每一次X光中，更多 从肺野的肋间间隙中选择100多块瓷砖。 因此，消除了来自肋骨结构的图像噪声。数学 不透明度的建模将被用来探索 要使用的功能，以及它们对 曝光和数字化噪音，这是我们工作中独有的。我们的 不透明度模型在图像协方差预测中的应用 与从实际数据中获得的结果相对应。这种类型的分析 将使我们更深入地了解每一项的价值和意义 特写。

Coal workers' pneumoconiosis (CWP), also known as black lung disease, exhibits rounded or irregular small opacities in standard chest x-rays caused by a physiological reaction to the deposition of coal dust in the lung. The disease is staged with two parameters: profusion, a measure of opacity concentration, and opacity size/shape. When diagnosing an x-ray, there is a great deal of variability in among radiologists in staging the disease. Consistent diagnoses are vital, however, because of medical and legal issues such as prevention of disease progression by early detection, and equitable compensation for afflicted workers. The goal of this research is to develop a method to minimize diagnostic variability by automatically staging CWP utilizing computer analysis of digitized chest x-rays. This work is a cooperative effort with the National Institute of Occupational Safety and Health (NIOSH, Morgantown, WV) which maintains a repository of all coal workers' x-rays and corresponding diagnostic and epidemiological data for the United States. No deterministic methodology exists for solving problems of pattern recognition such as the automated staging of CWP. We plan to apply statistical pattern recognition techniques utilizing new features derived from mathematical modeling of the digitized image. Pairwise linear discriminant analysis will be employed in dimensionality reduction and the development of a classifier based on an optimal subset of features. We are currently investigating features based on the spatial gray level dependence matrix, two-dimensional Fourier spectra, image morphology, and Gauss-Markov random fields. Our analysis will examine the dependence on context (spatial location) of features in classifier development, which has not been applied to this problem previously. Training and test set films will be obtained from the standard International Labour Organization set of pneumoconiosis films and from NIOSH films, digitized at the University of Pittsburgh. Diagnostic data on the films will be obtained from previous readers (usually two) and the University of Pittsburgh Department of Radiology. From each x-ray, more than 100 tiles are selected from the inter-rib spaces in the lung fields. Image noise from rib structures is therefore eliminated. Mathematical modeling of opacities will be employed to explore the appropriateness of the features to be used, as well as their robustness to variations in exposure and digitization noise, which is unique to our work. Our utilization of an opacity model in the prediction of image covariance corresponded to results obtained from actual data. This type of analysis will give much more insight into both the value and meaning of each feature.