Lo S C, Freedman M T, Lin J S, Mun S K
Radiology Department, Georgetown University Medical Center, Washington, DC.
J Digit Imaging. 1993 Feb;6(1):48-54. doi: 10.1007/BF03168418.
The potential advantages of using digital techniques instead of film-based radiography have been discussed extensively for the past 10 years. A major future application of digital techniques is computer-assisted diagnosis: the use of computer techniques to assist the radiologist in the diagnostic process. One aspect of this assistance is computer-assisted detection. The detection of small lung nodule has been recognized as a clinically difficult task for many years. Most of the literature has indicated that the rate for finding lung nodules (size range from 3 mm to 15 mm) is only approximately 65%, in those cases in which the undetected nodules could be found retrospectively. In recent published research, image processing techniques, such as thresholding and morphological analysis, have been used to enhance true-positive detection. However, these methods still produce many false-positive detections. We have been investigating the use of neural networks to distinguish true-positives nodule detections among those areas of interest that are generated from a signal enhanced image. The initial results show that the trained neural networks program can increase true-positive detections and moderately reduce the number of false-positive detections. The program reported here can perform three modes of lung nodule detection: thresholding, profile matching analysis, and neural network. This program is fully automatic and has been implemented in a DEC 5000/200 (Digital Equipment Corp, Maynard, MA) workstation. The total processing time for all three methods is less than 35 seconds. In this report, key image processing techniques and neural network for the lung nodule detection are described and the results of this initial study are reported.
在过去十年中，人们广泛讨论了使用数字技术而非基于胶片的射线照相术的潜在优势。数字技术未来的一个主要应用是计算机辅助诊断：利用计算机技术在诊断过程中协助放射科医生。这种协助的一个方面是计算机辅助检测。多年来，检测小的肺结节一直被认为是一项临床难题。大多数文献表明，在那些未检测到的结节可被追溯发现的病例中，发现肺结节（大小范围为3毫米至15毫米）的比率仅约为65%。在最近发表的研究中，图像处理技术，如阈值处理和形态分析，已被用于增强真阳性检测。然而，这些方法仍然会产生许多假阳性检测结果。我们一直在研究使用神经网络来区分从信号增强图像中生成的感兴趣区域中的真阳性结节检测。初步结果表明，经过训练的神经网络程序可以增加真阳性检测并适度减少假阳性检测的数量。这里报告的程序可以执行三种肺结节检测模式：阈值处理、轮廓匹配分析和神经网络。该程序是完全自动的，并且已在DEC 5000/200（数字设备公司，马萨诸塞州梅纳德）工作站上实现。所有三种方法的总处理时间不到35秒。在本报告中，描述了用于肺结节检测的关键图像处理技术和神经网络，并报告了这项初步研究的结果。
