PROCEEDINGS ARTICLE | March 14, 2011
Proc. SPIE. 7962, Medical Imaging 2011: Image Processing
KEYWORDS: Image processing algorithms and systems, Tissues, Magnetic resonance imaging, Image segmentation, Image processing, Image resolution, Image classification, Neuroimaging, 3D image processing, Brain
Segmentation of brain tumours in Magnetic Resonance (MR) images and classification of the tumour tissue into
vital, necrotic, and perifocal edematous areas is required in a variety of clinical applications. Manual delineation of
the tumour tissue boundaries is a tedious and error-prone task, and the results are not reproducible. Furthermore,
tissue classification mostly requires information of several MR protocols and contrasts. Here we present a nearly
automatic segmentation and classification algorithm for brain tumour tissue working on a combination of T1
weighted contrast enhanced (T1CE) images and fluid attenuated inversion recovery (FLAIR) images. Both
image types are included in MR brain tumour protocols that are used in clinical routine. The algorithm is
based on a region growing technique, hence it is fast (ten seconds on a standard personal computer). The only
required user interaction is a mouse click for providing the starting point. The region growing parameters are
automatically adapted in the course of growing, and if a new maximum image intensity is found, the region
growing is restarted. This makes the algorithm robust, i.e. independent of the given starting point in a certain
capture range. Furthermore, we use a lossless coarse-to-fine approach, which, together with the automatic
adaptation of the parameters, can avoid leakage of the region growing procedure. We tested our algorithm on
20 cases of human glioblastoma and meningioma. In the majority of the test cases we got satisfactory results.
