Segmentation of brain tumor parts in magnetic resonance images

The problem most frequently encountered in the practical processing of medical images consists in the lack of instruments enabling machine evaluation of the images. A typical example of this situation is perfusion analysis of brain tumor types. The first and very significant step lies in the segmentation of individual parts of the brain tumor; after segmentation, the rate of penetration by the applied contrast agent is observed in the parts. The common method, in which a high error rate has to be considered, is to mark these tumor portions manually. Within the second step of the segmentation procedure, the monitoring of perfusion in the segmented tissues is realized together with the correlation to model cases. The quality of brain tissue segmentation exerts significant influence on the quality of evaluation of perfusion parameters; consequently, the tumor type recognition is also influenced. This means that the design of a suitable, accurate, and reproducible method constitutes a critical point within perfusion analysis. In this context, reproducibility is an important aspect owing to the preservation of segmentation conditions in monitoring the development of a tumor in time. The authors describe classification methods enabling the segmentation of images acquired via magnetic resonance tomography.