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MICCAI
2005
Springer
14 years 6 months ago
Synthetic Ground Truth for Validation of Brain Tumor MRI Segmentation
Validation and method of comparison for segmentation of magnetic resonance images (MRI) presenting pathology is a challenging task due to the lack of reliable ground truth. We prop...
Marcel Prastawa, Elizabeth Bullitt, Guido Gerig
ECCV
2000
Springer
14 years 7 months ago
A Physically-Based Statistical Deformable Model for Brain Image Analysis
A probabilistic deformable model for the representation of brain structures is described. The statistically learned deformable model represents the relative location of head (skull...
Christophoros Nikou, Fabrice Heitz, Jean-Paul Arms...
CVBIA
2005
Springer
13 years 11 months ago
Shape Based Segmentation of Anatomical Structures in Magnetic Resonance Images
Standard image based segmentation approaches perform poorly when there is little or no contrast along boundaries of different regions. In such cases, segmentation is largely perfor...
Kilian M. Pohl, John W. Fisher III, Ron Kikinis, W...
CEC
2010
IEEE
13 years 3 months ago
Real-coded differential crisp clustering for MRI brain image segmentation
Abstract-- In this paper, a segmentation technique of multispectral magnetic resonance image of the brain using a new differential evolution based crisp clustering is proposed. Rea...
Indrajit Saha, Ujjwal Maulik, Sanghamitra Bandyopa...
CVBIA
2005
Springer
13 years 11 months ago
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
Abstract. Markov Random Fields (MRFs) are a popular and wellmotivated model for many medical image processing tasks such as segmentation. Discriminative Random Fields (DRFs), a dis...
Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bi...