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14 March 2011 Brain tumour segmentation and tumour tissue classification based on multiple MR protocols
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Proceedings Volume 7962, Medical Imaging 2011: Image Processing; 79622O (2011)
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
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.
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Astrid Franz, Stefanie Remmele, and Jochen Keupp "Brain tumour segmentation and tumour tissue classification based on multiple MR protocols", Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622O (14 March 2011);

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