Machine Learning
Shape quantization and recognition with randomized trees
Neural Computation
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
An incremental—learning neural network for the classification of remote—sensing images
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Discriminative, Semantic Segmentation of Brain Tissue in MR Images
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Hybrid deformable model for aneurysm segmentation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Spatial decision forests for MS lesion segmentation in multi-channel MR images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Regression forests for efficient anatomy detection and localization in CT studies
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Detection of type II endoleaks in abdominal aortic aneurysms after endovascular repair
Computers in Biology and Medicine
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Abdominal Aortic Aneurysm (AAA) is a local dilation of the Aorta that occurs between the renal and iliac arteries. The weakening of the aortic wall leads to its deformation and the generation of a thrombus. Recently, the procedure used for treatment involves the insertion of a endovascular prosthetic (EVAR), which has the advantage of being a minimally invasive procedure but also requires monitoring to analyze postoperative patient outcomes. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel active learning hybrid approach for the semi-automatic detection and segmentation of the lumen and the thrombus of the AAA, which uses image intensity features and discriminative Random Forest classfiers.