Lee Jeongjin, Kim Kyoung Won, Kim So Yeon, Shin Juneseuk, Park Kyung Jun, Won Hyung Jin, Shin Yong Moon
School of Computer Science and Engineering, Soongsil University, Dongjak-Gu, Seoul, Korea.
Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Songpa-ku, Seoul, Korea.
J Xray Sci Technol. 2015;23(3):275-88. doi: 10.3233/XST-150487.
Multi-phase CT images are obtained sequentially after the injection of contrast agents so that there is a large amount of local deformation between images due to the respiratory and heart motion. Therefore, a non-rigid registration technique is required in order to establish the anatomical correspondence between the multi-phase CT images for liver CAD (computer-aided diagnosis).
In this paper, we propose the automatic detection method of hepatocellular carcinomas using the non-rigid registration method of multi-phase CT images.
Global movements between multi-phase CT images are aligned by rigid registration based on normalized mutual information. Local deformations between multi-phase CT images are modeled by non-rigid registration based on B-spline deformable model. After the registration of multi-phase CT images, hepatocellular carcinomas are automatically detected by analyzing the original and subtraction information of the registered multi-phase CT images.
We applied our method to twenty five multi-phase CT datasets. Experimental results showed that the multi-phase CT images were accurately aligned. All of the hepatocellular carcinomas including small size ones in our 25 subjects were accurately detected using our method.
We conclude that our method is useful for detecting hepatocellular carcinomas.
多期CT图像是在注射造影剂后依次获取的，因此由于呼吸和心脏运动，图像之间存在大量局部变形。因此，为了在肝脏计算机辅助诊断（CAD）的多期CT图像之间建立解剖对应关系，需要一种非刚性配准技术。
本文提出一种利用多期CT图像的非刚性配准方法自动检测肝细胞癌的方法。
基于归一化互信息的刚性配准对齐多期CT图像之间的全局运动。基于B样条可变形模型的非刚性配准对多期CT图像之间的局部变形进行建模。在多期CT图像配准后，通过分析配准后的多期CT图像的原始信息和相减信息自动检测肝细胞癌。
我们将我们的方法应用于25个多期CT数据集。实验结果表明，多期CT图像被准确对齐。使用我们的方法，我们25名受试者中的所有肝细胞癌，包括小尺寸的肝细胞癌，都被准确检测到。
我们得出结论，我们的方法对检测肝细胞癌有用。
