Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, no...
Information processing in medical imaging : proceedings of the ... conference
Jan 1, 2015
This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons r...
Information processing in medical imaging : proceedings of the ... conference
Jan 1, 2015
Sequential learning techniques, such as auto-context, that applies the output of an intermediate classifier as contextual features for its subsequent classifier has shown impressive performance for semantic segmentation. We show that these methods ca...
Information processing in medical imaging : proceedings of the ... conference
Jan 1, 2015
Automatic medical image analysis systems often start from identifying the human body part contained in the image; Specifically, given a transversal slice, it is important to know which body part it comes from, namely "slice-based bodypart recognition...
Information processing in medical imaging : proceedings of the ... conference
Jan 1, 2015
Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount o...
Information processing in medical imaging : proceedings of the ... conference
Jan 1, 2015
Brain morphometry study plays a fundamental role in medical imaging analysis and diagnosis. This work proposes a novel framework for brain cortical surface classification using Wasserstein distance, based on uniformization theory and Riemannian optim...
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