Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Deviations from Normalcy for Brain Tumor Segmentation
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
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
Model-Based Brain and Tumor Segmentation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Overlap Prior for Variational Image Segmentation
International Journal of Computer Vision
Level-set segmentation of brain tumors using a threshold-based speed function
Image and Vision Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Synthetic ground truth for validation of brain tumor MRI segmentation
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic segmentation of non-enhancing brain tumors in magnetic resonance images
Artificial Intelligence in Medicine
Anisotropic diffusion of multivalued images with applications to color filtering
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Hi-index | 0.00 |
A method is presented to segment brain tumors in multiparametric MR images via robustly propagating reliable statistical tumor information which is extracted from training tumor images using a support vector machine (SVM) classification method. The propagation of reliable statistical tumor information is implemented using a graph theoretic approach to achieve tumor segmentation with local and global consistency. To limit information propagation between image voxels of different properties, image boundary information is used in conjunction with image intensity similarity and anatomical spatial proximity to define weights of graph edges. The proposed method has been applied to 3D multi-parametric MR images with tumors of different sizes and locations. Quantitative comparison results with state-of-the-art methods indicate that our method can achieve competitive tumor segmentation performance.