Automatic segmentation of cortical region of the brain from MR images
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
The Use of Geometric Algebra for 3D Modeling and Registration of Medical Data
Journal of Mathematical Imaging and Vision
Fuzzy spatial growing for glioblastoma multiforme segmentation on brain magnetic resonance imaging
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A generative model for brain tumor segmentation in multi- modal images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Segmentation and volume representation based on spheres for non-rigid registration
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
3D brain tumor segmentation using fuzzy classification and deformable models
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Surface approximation using growing self-organizing nets and gradient information
Applied Bionics and Biomechanics
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Combining image segmentation based on statistical classification with a geometric prior has been shown to significantly increase robustness and reproducibility. Using a probabilistic geometric model and image registration serves both initialization of probability density functions and definition of spatial constraints. A strong spatial prior, however, prevents segmentation of structures that are not part of the model.Our driving application is the segmentation of brain tissue and tumors from three-dimensional magnetic resonance imaging (MRI). Our goal is a high-quality segmentation of both healthy tissue and tumor. We present an extension to an existing expectation maximization (EM) segmentation algorithm that modifies a probabilistic brain atlas with an individual subject's information about tumor location obtained from subtraction of post- and pre-contrast MRI. The new method handles various types of pathology, space-occupying mass tumors and infiltr ating changes like edema. Preliminary results on five cases presenting tumor types with very different characteristics demonstrate the potential of the new technique for clinical routine use for planning and monitoring in neurosurgery, radiation oncology, and radiology.