Segmentation of intracranial tumors in Magnetic Resonance (MR) data sets and classification of the tumor
tissue into vital, necrotic, and perifocal edematous areas is required in a variety of clinical applications. Manual
delineation of the tumor tissue boundaries is a tedious and error-prone task, and reproducibility is problematic.
Furthermore, tissue classification mostly requires information of several MR protocols and contrasts. Here we
present a nearly automatic segmentation and classification algorithm for intracranial tumor tissue working on a
combination of T1 weighted contrast enhanced (T1CE) and fluid attenuated inversion recovery (FLAIR) data
sets. Both data types are included in MR intracranial tumor protocols that are used in clinical routine. The
algorithm is based on a region growing technique. The main required user interaction is a mouse click to provide
the starting point. The region growing thresholds are automatically adapted to the requirements of the actual
data sets. If the segmentation result is not fully satisfying, the user is allowed to adapt the algorithmic parameters
for final fine-tuning. We developed a user interface, where the data sets can be loaded, the segmentation can be
started by a mouse click, the parameters can be amended, and the segmentation results can be saved. With this
user interface, our segmentation tool can be used in the hospital on an image processing workstation or even
directly on the MR scanner. This enables an extensive validation study. On the 20 clinical test cases of human
intracranial tumors we investigated so far, the results were satisfying in 85% of the cases.