Chang Y H, Zheng B, Gur D
Department of Radiology, University of Pittsburgh, Pennsylvania 15261-0001, USA.
Invest Radiol. 1996 Mar;31(3):146-53. doi: 10.1097/00004424-199603000-00005.
A simple and effective computerized detection scheme was developed to identify suspicious mass regions in digitized mammograms.
This method identifies a maximum of five suspicious mass regions per image and was tested with a database of 510 images, including 162 verified masses. It includes a series of five rule-based processes that select one region with each of the following characteristics: 1) a global minimum of optical density in a smoothed image; 2) a local minimum of optical density in the original image; 3) a local minimum of optical density in a filtered image; 4) a small "mass" of low contrast; and 5) a small "mass" of high contrast.
This multi-stage process achieved a sensitivity of 95% while limiting false-positive detection rates to below an average of two per image.
Because this method limits the initial number of suspicious mass regions while retaining high sensitivity, it may be applicable to clinically usable computer-aided diagnosis schemes.
开发了一种简单有效的计算机化检测方案，用于识别数字化乳腺X线摄影中的可疑肿块区域。
该方法每张图像最多识别5个可疑肿块区域，并在包含510幅图像（其中162个为经证实的肿块）的数据库上进行了测试。它包括一系列基于五条规则的处理过程，分别选择具有以下特征的一个区域：1）平滑图像中光学密度的全局最小值；2）原始图像中光学密度的局部最小值；3）滤波图像中光学密度的局部最小值；4）低对比度的小“肿块”；5）高对比度的小“肿块”。
这个多阶段过程实现了95%的灵敏度，同时将假阳性检测率限制在平均每张图像低于两个。
由于该方法在保持高灵敏度的同时限制了可疑肿块区域的初始数量，它可能适用于临床可用的计算机辅助诊断方案。
