IEEE Trans Image Process. 2015 Dec;24(12):5315-29. doi: 10.1109/TIP.2015.2481326. Epub 2015 Sep 23.
Liver segmentation is still a challenging task in medical image processing area due to the complexity of the liver's anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and inter-relationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.
肝脏分割在医学图像处理领域仍然是一项具有挑战性的任务，这是由于肝脏解剖结构的复杂性、与邻近器官对比度低以及存在病变等原因。本研究旨在开发和验证一种用于 CT 图像中肝脏分割的自动化方法。所提出的框架由三个步骤组成：1）预处理；2）初始化；3）分割。在第一步中，基于主成分分析构建统计形状模型，并使用曲率各向异性扩散滤波对输入图像进行平滑处理。在第二步中，使用阈值和欧几里得距离变换移动平均形状模型，以在测试图像中获得粗略位置，然后局部且迭代地将初始网格变形到粗略边界，该边界约束为接近描述解剖变异的形状子空间。最后，为了准确检测肝脏表面，提出了可变形图割，它有效地整合了输入图像和初始化表面的特性和相互关系。该方法在两个公开数据库 Sliver07 和 3Dircadb 中的 50 个 CT 扫描图像上进行了评估。实验结果表明，该方法对于检测肝脏表面是有效且准确的。
