Li L, Zheng Y, Kallergi M, Clark R A
Department of Radiology, H. Lee Moffitt Cancer Research Institute, University of South Florida College of Medicine, Tampa 33612, USA.
Acad Radiol. 2001 Jul;8(7):629-38. doi: 10.1016/S1076-6332(03)80688-8.
The authors performed this study to evaluate an algorithm developed to help identify lungs on chest radiographs.
Forty clinical posteroanterior chest radiographs obtained in adult patients were digitized to 12-bit gray-scale resolution. In the proposed algorithm, the authors simplified the current approach of edge detection with derivatives by using only the first derivative of the horizontal and/or vertical image profiles. In addition to the derivative method, pattern classification and image feature analysis were used to determine the region of interest and lung boundaries. Instead of using the traditional curve-fitting method to delineate the lung, the authors applied an iterative contour-smoothing algorithm to each of the four detected boundary segments (costal, mediastinal, lung apex, and hemidiaphragm edges) to form a smooth lung boundary.
The algorithm had an average accuracy of 96.0% for the right lung and 95.2% for the left lung and was especially useful in the delineation of hemidiaphragm edges. In addition, it took about 0.775 second per image to identify the lung boundaries, which is much faster than that of other algorithms noted in the literature.
The computer-generated segmentation results can be used directly in the detection and compensation of rib structures and in lung nodule detection.
作者开展本研究以评估一种为帮助在胸部X光片上识别肺部而开发的算法。
对成年患者的40张临床后前位胸部X光片进行数字化处理，分辨率为12位灰度。在所提出的算法中，作者仅使用水平和/或垂直图像轮廓的一阶导数简化了当前的导数边缘检测方法。除了导数方法外，还使用模式分类和图像特征分析来确定感兴趣区域和肺边界。作者没有使用传统的曲线拟合方法来描绘肺部，而是对检测到的四个边界段（肋骨、纵隔、肺尖和半膈肌边缘）分别应用迭代轮廓平滑算法以形成平滑的肺边界。
该算法对右肺的平均准确率为96.0%，对左肺为95.2%，在描绘半膈肌边缘方面特别有用。此外，识别肺边界每张图像大约需要0.775秒，这比文献中提到的其他算法要快得多。
计算机生成的分割结果可直接用于肋骨结构的检测和补偿以及肺结节检测。
