Jeroen A. W. Tielbeek

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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)
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)
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)
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)
This paper studies a novel method to compensate for respiratory and peristaltic motions in abdominal dynamic contrast enhanced magnetic resonance imaging. The method consists of two steps: 1) expiration-phase "template" construction and retrospective gating of the data to the template; and 2) nonrigid registration of the gated volumes. Landmarks annotated(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)
OBJECTIVE To determine the stability and reproducibility of the sodium magnetic resonance imaging (MRI) signal measured in the articular cartilage of the knee in both healthy volunteers and osteoarthritis (OA) patients. DESIGN This was a prospective Research Ethics Committee approved study that acquired sodium and proton MRI data from 15 subjects with OA(More)