Adaptive metamorphs model for 3D medical image segmentation.
Adaptive metamorphs model for 3D medical image segmentation.
复制标题
用于 3D 医学图像分割的自适应变形模型。
DOI：
10.1007/978-3-540-75757-3_37
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Axel,Leon
中科院分区：
文献类型：
--
作者：
Huang,Junzhou;Huang,Xiaolei;Metaxas,Dimitris;Axel,Leon
在本文中，我们介绍了一个自适应的基于模型的分割框架，其中的边缘和区域信息的集成和自适应使用，而一个实体模型向对象边界变形。我们的3D分割方法源于Metamorphs变形模型[1]。我们的工作的主要新奇在于，而不是在整个3D体积中执行分割，我们提出了基于模型的分割在一个自适应变化的感兴趣的子体积。子体积是基于演化对象模型的外观统计来确定的，并且在子体积内，可以获得更准确的和对象特定的边缘和区域信息。这种用于计算边缘和对象区域信息的局部和自适应方案使我们的分割解决方案更有效，对图像噪声，伪影和强度不均匀性更具鲁棒性。模型变形的外力是在一个变分框架中推导出来的，该框架包括基于边缘和基于区域的能量项，同时考虑到自适应变化的环境。我们证明了我们的方法的性能，通过广泛的实验，使用心脏MR和肝脏CT图像。
In this paper, we introduce an adaptive model-based segmentation framework, in which edge and region information are integrated and used adaptively while a solid model deforms toward the object boundary. Our 3D segmentation method stems from Metamorphs deformable models [1]. The main novelty of our work is in that, instead of performing segmentation in an entire 3D volume, we propose model-based segmentation in an adaptively changing subvolume of interest. The subvolume is determined based on appearance statistics of the evolving object model, and within the subvolume, more accurate and object-specific edge and region information can be obtained. This local and adaptive scheme for computing edges and object region information makes our segmentation solution more efficient and more robust to image noise, artifacts and intensity inhomogeneity. External forces for model deformation are derived in a variational framework that consists of both edge-based and region-based energy terms, taking into account the adaptively changing environment. We demonstrate the performance of our method through extensive experiments using cardiac MR and liver CT images.
DOI：
10.1109/34.1000236
发表时间：
2002-05-01
影响因子：
23.6
作者：
Comaniciu, D;Meer, P
通讯作者：
Meer, P