Robust three-dimensional object definition in CT and MRI.
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.