Machine Learning
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Entangled decision forests and their application for semantic segmentation of CT images
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Detecting and classifying linear structures in mammograms using random forests
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Detection, grading and classification of coronary stenoses in computed tomography angiography
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Focal biologically inspired feature for glaucoma type classification
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Biological indexes based reflectional asymmetry for classifying cutaneous lesions
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
X-ray categorization and spatial localization of chest pathologies
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Fast multiple organ detection and localization in whole-body MR Dixon sequences
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Texture anisotropy in 3-D images
IEEE Transactions on Image Processing
Three-Dimensional Nonlinear Invisible Boundary Detection
IEEE Transactions on Image Processing
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Accurate diagnosis of Crohn's disease (CD) has emerged as an important medical challenge. Because current Magnetic resonance imaging (MRI) analysis approaches rely on extensive manual segmentation for an accurate analysis, we propose a method for the automatic identification and localization of regions in abdominal MR volumes that have been affected by CD. Our proposed approach will serve to augment results from colonoscopy, the current reference standard for CD diagnosis. Intensity statistics, texture anisotropy and shape asymmetry of the 3D regions are used as features to distinguish between diseased and normal regions. Particular emphasis is laid on a novel entropy based asymmetry calculation method. Experiments on real patient data show that our features achieve a high level of accuracy and perform better than two competing methods.