Bland P H, Meyer C R
Department of Radiology, University of Michigan Hospitals, Ann Arbor 48109-0553, USA.
Med Phys. 1996 Jan;23(1):99-107. doi: 10.1118/1.597686.
This work describes the application of an object definition algorithm to the medical imaging environment for the task of automated detection of anatomical boundaries in three dimensions in the presence of low spatial frequency nonstationarities. We have chosen the Liou-Jain algorithm and have modified it for use with 3D medical image datasets and extended it by including a recruitment operator that corrects for the algorithm's inherent volume underestimation. The algorithm avoids problems in both traditional statistical segmentation and 2D techniques and elegantly bridges the gap between traditional gradient-based edge finding and regression-based segmentation techniques. Results are shown for MRI datasets from the human abdomen and brain and for a CT dataset of a liver tumor, as well as an MRI scan of a glioma in a rat brain. For comparison, the human abdomen dataset was processed by a multivariate, statistical classifier. The results demonstrate the statistical technique's susceptibility to low spatial frequency nonstationarities due to rf field inhomogeneity; the Liou-Jain algorithm is shown to be immune to this effect. Further, the results show spatial consistency as a result of inherent characteristics of the algorithm. Volumes identified by the algorithm are visualized and assessed qualitatively in three dimensions. Quantitative accuracy of the algorithm's volume estimates is assessed by the use of a phantom. This work demonstrates that this technique is effective in automatically detecting anatomical organ and lesion surfaces in 3D medical datasets that are corrupted by low spatial frequency nonstationarity and in obtaining volume estimates.
这项工作描述了一种对象定义算法在医学成像环境中的应用，该算法用于在存在低空间频率非平稳性的情况下自动检测三维解剖边界。我们选择了刘 - 贾因算法，并对其进行了修改以用于3D医学图像数据集，并通过包含一个校正算法固有体积低估的募集算子对其进行了扩展。该算法避免了传统统计分割和二维技术中的问题，并巧妙地弥合了传统基于梯度的边缘检测和基于回归的分割技术之间的差距。展示了来自人类腹部和大脑的MRI数据集、肝脏肿瘤的CT数据集以及大鼠脑部胶质瘤的MRI扫描结果。作为比较，人类腹部数据集由多变量统计分类器进行处理。结果表明，由于射频场不均匀性，统计技术对低空间频率非平稳性敏感；而刘 - 贾因算法对这种影响具有免疫力。此外，结果显示出该算法固有特性所导致的空间一致性。对算法识别出的体积进行三维可视化并进行定性评估。通过使用体模来评估算法体积估计的定量准确性。这项工作表明，该技术在自动检测受低空间频率非平稳性影响的3D医学数据集中的解剖器官和病变表面以及获得体积估计方面是有效的。
