Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen 518055, China ; Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China.
Comput Math Methods Med. 2013;2013:834192. doi: 10.1155/2013/834192. Epub 2013 Nov 14.
Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.
非刚性图像配准是各种医学图像处理和分析应用的前提。由于呼吸运动，胸部图像配准已经得到了广泛的研究。最近，尺度不变特征变换（SIFT）已被用于医学图像配准，并取得了有希望的结果。然而，SIFT 易于检测斑点特征。斑点关键点通常在平滑区域中检测到，这些区域可能包含很少的诊断点。一般来说，医学图像中使用的诊断点通常是血管交叉点、血管端点和组织边界点，这些点提供了关于血管的丰富信息，可以准确反映肺部的运动。这些点通常具有较高的梯度，与斑点关键点相反，可以通过 Harris 检测到。在这项工作中，我们提出了一种混合特征检测方法，该方法可以基于 Harris 和 SIFT 有效地检测肺部的组织特征。此外，还提出了一种可以去除不匹配地标点的新方法。使用所提出的算法对一系列胸部 CT 图像进行了测试，定量和定性评估表明，我们的方法在统计学上明显优于传统的 SIFT 方法，尤其是在呼吸过程中肺部发生大变形的情况下。
