Functional Segmentation of Renal DCE-MRI Sequences Using Vector Quantization Algorithms
In dynamic contrast-enhanced magnetic resonance imaging, segmentation of internal kidney structures like cortex, medulla and cavities is essential for functional assessment. To avoid fastidious and time-consuming manual segmentation, semi-automatic methods have been recently developed. Some of them use the differences between temporal contrast evolution in each anatomical region to perform functional segmentation. We test two methods where pixels are classified according to their time-intensity evolution. They both require a vector quantization stage with some unsupervised learning algorithm (K-means or Growing Neural Gas with targeting). Three or more classes are thus obtained. In the first case the method is completely automatic. In the second case, a restricted intervention by an observer is required for merging. As no ground truth is available for result evaluation, a manual anatomical segmentation is considered as a reference. Some discrepancy criteria like overlap, extra pixels and similarity index are computed between this segmentation and a functional one. The same criteria are also evaluated between the reference and another manual segmentation. Results are comparable for the two types of comparisons, proving that anatomical segmentation can be performed using functional information.
vector quantization image segmentation biomedical image processing biomedical magnetic resonance imaging image sequence analysis clustering methods