User-steered image segmentation paradigms: live wire and live lane
Graphical Models and Image Processing
Markov random field modeling in image analysis
Markov random field modeling in image analysis
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Model-based three-dimensional medical image segmentation
Model-based three-dimensional medical image segmentation
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Automated Segmentation of Brain Tumors in MRI Using Force Data Clustering Algorithm
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
Effective fuzzy c-means based kernel function in segmenting medical images
Computers in Biology and Medicine
Novel segmentation algorithm in segmenting medical images
Journal of Systems and Software
Robust kernel FCM in segmentation of breast medical images
Expert Systems with Applications: An International Journal
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
Computers in Biology and Medicine
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A framework is proposed for the segmentation of brain tumors from MRI. Instead of training on pathology, the proposed method trains exclusively on healthy tissue. The algorithm attempts to recognize deviations from normalcy in order to compute a fitness map over the image associated with the presence of pathology. The resulting fitness map may then be used by conventional image segmentation techniques for honing in on boundary delineation. Such an approach is applicable to structures that are too irregular, in both shape and texture, to permit construction of comprehensive training sets. The technique is an extension of EM segmentation that considers information on five layers: voxel intensities, neighborhood coherence, intra-structure properties, inter-structure relationships, and user input. Information flows between the layers via multi-level Markov random fields and Bayesian classification. A simple instantiation of the framework has been implemented to perform preliminary experiments on synthetic and MRI data.