Huang Junzhou, Huang Xiaolei, Metaxas Dimitris, Axel Leon
Division of Computer and Information Sciences, Rutgers University, NJ, USA.
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):302-10. doi: 10.1007/978-3-540-75757-3_37.
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. 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.
在本文中，我们介绍了一种基于自适应模型的分割框架，其中在实体模型向物体边界变形的同时，边缘和区域信息被整合并自适应地使用。我们的三维分割方法源于变形可变形模型。我们工作的主要新颖之处在于，我们不是在整个三维体积中进行分割，而是在一个自适应变化的感兴趣子体积中提出基于模型的分割。该子体积是根据演化物体模型的外观统计确定的，并且在该子体积内，可以获得更准确且特定于物体的边缘和区域信息。这种用于计算边缘和物体区域信息的局部自适应方案使我们的分割解决方案对图像噪声、伪影和强度不均匀性更高效且更鲁棒。模型变形的外力是在一个变分框架中推导出来的，该框架由基于边缘和基于区域的能量项组成，同时考虑了自适应变化的环境。我们通过使用心脏磁共振成像和肝脏计算机断层扫描图像的大量实验来证明我们方法的性能。
