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Segmentation of Brain Tumors from MRI using Adaptive Thresholding and Graph Cut Algorithm

Development of methods for automatic brain tumor segmentation remains one of the most challenging tasks in processing of medical data. Exact segmentation could improve the diagnostics, as for example the time evaluation of the tumor volume. However, manual segmentation in magnetic resonance data is a time-consuming task. We present a method of automatic tumor segmentation in magnetic resonance images which consists of several steps. In the first step high intense cranium is removed. In the next step parameters of the image are derived using the method “Mixture of Gaussians”. These parameters control the morphological reconstruction (proposed by Luc Vincent 1993). The morphological reconstruction produces binary mask which is used in the last step of the segmentation: graph cut segmentation. First results of this method are presented in this paper.

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