Oğul Hasan, Oğul B Buket, Ağıldere A Muhteşem, Bayrak Tuncay, Sümer Emre
Department of Computer Engineering, Başkent University, Ankara, Turkey.
Akgun Software Company, Ankara, Turkey.
Comput Methods Programs Biomed. 2016 Apr;127:174-84. doi: 10.1016/j.cmpb.2015.12.006. Epub 2015 Dec 25.
A major difficulty with chest radiographic analysis is the invisibility of abnormalities caused by the superimposition of normal anatomical structures, such as ribs, over the main tissue to be examined. Suppressing the ribs with no information loss about the original tissue would therefore be helpful during manual identification or computer-aided detection of nodules on a chest radiographic image. In this study, we introduce a two-step algorithm for eliminating rib shadows in chest radiographic images. The algorithm first delineates the ribs using a novel hybrid self-template approach and then suppresses these delineated ribs using an unsupervised regression model that takes into account the change in proximal thickness (depth) of bone in the vertical axis. The performance of the system is evaluated using a benchmark set of real chest radiographic images. The experimental results determine that proposed method for rib delineation can provide higher accuracy than existing methods. The knowledge of rib delineation can remarkably improve the nodule detection performance of a current computer-aided diagnosis (CAD) system. It is also shown that the rib suppression algorithm can increase the nodule visibility by eliminating rib shadows while mostly preserving the nodule intensity.
胸部X光片分析的一个主要困难在于，正常解剖结构（如肋骨）叠加在主要待检查组织上会导致异常情况不可见。因此，在胸部X光图像上进行手动识别或计算机辅助检测结节时，在不损失原始组织信息的情况下抑制肋骨会有所帮助。在本研究中，我们引入了一种两步算法来消除胸部X光图像中的肋骨阴影。该算法首先使用一种新颖的混合自模板方法勾勒出肋骨，然后使用一个无监督回归模型抑制这些勾勒出的肋骨，该模型考虑了骨骼在垂直轴上近端厚度（深度）的变化。使用一组真实胸部X光图像的基准集来评估该系统的性能。实验结果表明，所提出的肋骨勾勒方法比现有方法具有更高的准确性。肋骨勾勒的知识可以显著提高当前计算机辅助诊断（CAD）系统的结节检测性能。研究还表明，肋骨抑制算法可以通过消除肋骨阴影同时大多保留结节强度来提高结节的可见性。
