Automatic segmentation of non-enhancing brain tumors in magnetic resonanceimages

Tumor segmentation from magnetic resonance (MR) images may aid in tumor tre atment by tracking the progress of tumor growth and/or shrinkage. In this p aper we present the first automatic segmentation method which separates non -enhancing brain tumors from healthy tissues in MR images to aid in the tas k of tracking tumor size over time. The MR feature images used for the segm entation consist of three weighted images (T1, T2 and proton density (PD)) for each axial slice through the head. An initial segmentation is computed using an unsupervised fuzzy clustering algorithm. Then, integrated domain k nowledge and image processing techniques contribute to the final tumor segm entation. They are applied under the control of a knowledge-based system. T he system knowledge was acquired by training on two patient volumes (14 ima ges). Testing has shown successful tumor segmentations on four patient volu mes (31 images). Our results show that we detected all six non-enhancing br ain tumors, located tumor tissue in 35 of the 36 ground truth (radiologist labeled) slices containing tumor and successfully separated tumor regions f rom physically connected CSF regions in all the nine slices. Quantitative m easurements are promising as correspondence ratios between ground truth and segmented tumor regions ranged between 0.368 and 0.871 per volume, with pe rcent match ranging between 0.530 and 0.909 per volume. (C) 2001 Elsevier S cience B,V. All rights reserved.