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We propose a new and clinically oriented approach to perform atlas-based segmentation of brain tumor images. A mesh-free method is used to model tumor-induced soft tissue deformations in a healthy brain atlas image with subsequent registration of the modified atlas to a pathologic patient image. The atlas is seeded with a tumor position prior and tumor growth simulating the tumor mass effect is performed...
In this paper, we study the effects of model complexity on the accuracy of the results in the computer simulation of transcranial magnetic stimulation (TMS). The method has been extensively used in the last decade as a noninvasive technique to excite neurons in the brain by inducing weak electric currents in the tissue and proved to be a very promising alternative for currently invasive treatments...
A validation framework for MR image segmentation is proposed in this paper. It includes three stages: intensity inhomogeneity (IIH) correction, noise suppression without blurring structures and tissue classification. Based on MR brain images, in the first stage, an improved process is used to implement IIH correction. Subsequently, a new enhancement method on moments for noise removal and edge sharpening...
A method is proposed for modeling and classification of White Matter fiber tracts in the brain. The presented scheme uses classification trees in conjunction with spatial representation of the individual fibers, in order to capture the characteristic behavior of fibers belonging to a specific anatomical structure. The method is characterized by high classification speed, under 3 seconds for all the...
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