Automating Segmentation of Dual-Echo MR Head Data
IPMI '91 Proceedings of the 12th International Conference on Information Processing in Medical Imaging
A user-guided tool for efficient segmentation of medical image data
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Adaptive Template Moderated Spatially Varying Statistical Classification
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Quantification of MS Lesions in 3D MRI Brain Data Sets: Validation of INSECT
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Level-Set Evolution with Region Competition: Automatic 3-D Segmentation of Brain Tumors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Multi-Modality Image Registration Maximization of Mutual Information
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Automatic segmentation of age-related macular degeneration in retinal fundus images
Computers in Biology and Medicine
The Use of Geometric Algebra for 3D Modeling and Registration of Medical Data
Journal of Mathematical Imaging and Vision
Detecting pathologies with homology algorithms in magnetic resonance images of brain
Machine Graphics & Vision International Journal
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation
Computer Vision and Image Understanding
A review of atlas-based segmentation for magnetic resonance brain images
Computer Methods and Programs in Biomedicine
Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Information Sciences: an International Journal
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 of sought structures 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. In practical applications, we encounter either the presentation of new objects that cannot be modeled with a spatial prior or regional intensity changes of existing structures not explained by 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 healthy tissue and a precise delineation of tumor boundaries. 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 infiltrating 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.