Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, 514-8507, Mie, Japan.
J Digit Imaging. 2012 Feb;25(1):148-54. doi: 10.1007/s10278-011-9396-8.
It is difficult to detect sentinel lymph nodes (SLNs) around an injection point of radiopharmaceuticals mapped in lymphoscintigrams. The purpose of this study was to develop a computer-aided detection (CAD) scheme for SLNs by a subtraction technique using the symmetrical property in the mapped injection point. Our database consisted of 78 lymphoscintigrams with 86 SLNs. In our CAD scheme, the mapped injection point of radiopharmaceuticals was first segmented from the lymphoscintigram using a gray-level thresholding technique. Lymphoscintigram was then divided into four regions by vertical and horizontal straight lines through the center of the segmented injection point. One of the four divided regions was defined as the target region. The correlation coefficients based on pixel values were calculated between the target region and each of the other three regions. The region with the highest correlation coefficient among three regions was selected as the similar region to the target region. The values of pixels on the target region were subtracted by the values of the corresponding pixels on the similar region. This procedure was repeated until every divided region had been used as target region. SLNs were segmented by applying a gray-level thresholding technique to the subtracted image. With our CAD scheme, sensitivity and the number of false positives were 95.3% (82/86) and 2.51 per image, respectively. Our CAD scheme achieved a high level of detection accuracy, and would have a great potential in assisting physicians to detect SLNs in lymphoscintigrams.
在淋巴闪烁图中映射的放射性药物注射点周围检测前哨淋巴结 (SLN) 较为困难。本研究旨在通过使用映射注射点的对称特性的减法技术开发一种用于 SLN 的计算机辅助检测 (CAD) 方案。我们的数据库由 78 张淋巴闪烁图和 86 个 SLN 组成。在我们的 CAD 方案中,首先使用灰度阈值技术从淋巴闪烁图中分割出放射性药物的映射注射点。然后,通过穿过分割注射点中心的垂直线和水平线将淋巴闪烁图分为四个区域。这四个分区中的一个被定义为目标区域。基于像素值计算目标区域与其他三个区域中每一个区域之间的相关系数。在三个区域中具有最高相关系数的区域被选择为与目标区域相似的区域。将目标区域上的像素值减去相似区域上的相应像素值。此过程重复进行,直到所有分区都已用作目标区域。通过对减去的图像应用灰度阈值技术来分割 SLN。使用我们的 CAD 方案,灵敏度和假阳性数量分别为 95.3%(82/86)和每幅图像 2.51。我们的 CAD 方案实现了高水平的检测准确性,并且在协助医生在淋巴闪烁图中检测 SLN 方面具有很大的潜力。