Jeroen A. W. Tielbeek

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Registration of images in the presence of intra-image signal fluctuations is a challenging task. The definition of an appropriate objective function measuring the similarity between the images is crucial for accurate registration. This paper introduces an objective function that embeds local phase features derived from the monogenic signal in the modality(More)
We propose an information processing pipeline for segmenting parts of the bowel in abdominal magnetic resonance images that are affected with Crohn's disease. Given a magnetic resonance imaging test volume, it is first oversegmented into supervoxels and each supervoxel is analyzed to detect presence of Crohn's disease using random forest (RF) classifiers.(More)
Increasing incidence of Crohn's disease (CD) in the Western world has made its accurate diagnosis an important medical challenge. The current reference standard for diagnosis, colonoscopy, is time-consuming and invasive while magnetic resonance imaging (MRI) has emerged as the preferred noninvasive procedure over colonoscopy. Current MRI approaches assess(More)
Our proposed method combines semi supervised learning (SSL) and active learning (AL) for automatic detection and segmentation of Crohn's disease (CD) from abdominal magnetic resonance (MR) images. Random forest (RF) classifiers are used due to fast SSL classification and capacity to interpret learned knowledge. Query samples for AL are selected by a novel(More)
Magnetic resonance imaging is increasingly used for abdominal evaluation and is more and more considered as the optimal imaging technique for detection of mural inflammation in patients with Crohn's disease. Grading the disease activity is important in daily clinical practice to monitor the medical treatment and is assessed by evaluating different magnetic(More)
We propose a active learning (AL) approach to segment Crohn's disease (CD) affected regions in abdominal magnetic resonance (MR) images. Our label query strategy is inspired from the principles of visual saliency which has similar considerations for choosing the most salient region. These similarities are encoded in a graph using classification maps and low(More)