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Many biomedical applications require detection of curvilinear networks in images, and would benefit from automatic or semiautomatic segmentation to allow high-throughput measurements. Here we discuss a contrast independent approach to identify curvilinear structures based on oriented phase congruency, the Phase Congruency Tensor. We show that the proposed approach is largely insensitive to intensity...
The human brain with all its faculties and intricacies has fascinated many generations of researchers [1] and will likely be the final frontier of science. Understanding the principles underlying the brain's higher-order cognitive functions is indeed a major challenge and will profoundly impact our views on what defines a human being. On a more down-to-earth level, knowledge of the structure, function,...
Better utilizing the vast amount of valuable information stored in the medical imaging databases is always an interesting research area, and one way is to retrieve similar images as a reference dataset to assist the diagnosis. Distance metric is a core component in image retrieval; and in this paper, we propose a new learning-based distance metric design, based on regression and classification techniques...
Morphology of bones, teeth, and some particular structures are widely used for categorizing species and studying their evolution. In this paper, we used groupwise registration to provide a representative image from the set of the image samples that represents its typical morphology. We also provided perturbation map which indicates the deviation of each point through the ensemble. These images support...
We proposed a local scale-based Hessian analysis method for automated lymph node detection in contrast-enhanced abdominopelvic CT scans. First, spine and pelvic girdle were automatically segmented to locate the abdominopelvic region. Blood vessels were then segmented to narrow the search region to the perivascular space where lymph nodes are located. Lymph node candidates were generated by scale-based...
Lobe detection from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. We present a fully automated method for segmenting the pulmonary lobes. We first build a lobar approximation by applying a watershed transform to a vesselness density filter, using seed points generated from segmentation and...
Respiratory disorders in premature are outlined as one of the major causes of newborn morbidity. Therefore, estimation in utero of the risk that a particular newborn will present respiratory distress syndrome is critical in perinatal medicine. Fetal lung maturity assessment by non-invasive methods is an unsolved problem since 80's. There has been a significant effort reporting that ultrasound images...
The identification of anatomical landmarks in the brain is an important task in registration and morphometry. The manual identification and labelling of these landmarks is very time consuming and prone to observer errors, especially when large datasets must be analysed. In this paper we present an approach that describes landmarks based on their intrinsic geometry, rather than their intensity patterns...
Magnetic resonance angiography (MRA) provides a noninvasive means to detect the presence, location and severity of atherosclerosis throughout the vascular system. In such studies, and especially those in the coronary arteries, the vessel luminal area is typically measured at multiple cross-sectional locations along the course of the artery. The advent of fast volumetric imaging techniques covering...
We consider the problem of processing high angular resolution diffusion images described by orientation distribution functions (ODFs). Prior work showed that several processing operations, e.g., averaging, interpolation and filtering, can be reduced to averaging in the space of ODFs. However, this approach leads to anatomically erroneous results when the ODFs to be processed have very different orientations...
A popular method for the segmentation of somewhat spherical structures (e.g. certain types of tumors, lymph nodes, lung nodules) from 3D medical images is sending out radial rays from a central point and determining the most likely radius for each ray, resulting in a closed surface. Besides satisfying some image based criteria, a regularization term or shape prior typically ensures a smooth contour...
We propose a new multivariate method, partial sparse canonical correlation analysis (PSCCA), for computing the statistical comparisons needed by population studies in medical imaging. PSCCA is a multivariate generalization of linear regression that allows one to statistically parameterize imaging studies in terms of multiple views of the population (e.g., the full collection of measurements taken...
Our group is developing a method based on 3D high-frequency ultrasound (HFU) and 3D quantitative ultrasound (QUS) to help pathologists detect micrometastases in freshly-excised lymph nodes of patients with histologically-proven primary cancer. From a signal and image processing perspective, we report on our efforts to acquire and classify lymph-node tissue based on 3D QUS parameter estimates. We evaluated...
A major limitation of graph cuts for the segmentation of large 2D image sequences is its interactive nature. The user has to provide seeds for almost every image frame to get an accurate segmentation. Straightforward approaches like direct copying of seeds provided in the first frame to other frames of the sequence fail in cases of great contrast or topo-logical changes that occur when there is a...
Medical imaging has developed a key role in drug development. Quantitative imaging can provide biomarkers that can help with key aspects of drug development, including assessing target engagement, selecting drug doses, producing evidence of efficacy, and monitoring safety. While it remains the case that drugs will only be approved by regulators if they provide clinical benefit in how people feel,...
We propose and evaluate a new block-matching strategy for rigid-body registration of multimodal or multisequence medical images. The classical algorithm first matches points of both images by maximizing the iconic similarity of blocks of voxels around them, then estimates the rigid-body transformation best superposing these matched pairs of points, and iterates these two steps until convergence. In...
We investigate saddle points in 3D cardiac images. We do so by improving a critical point detection algorithm, the 3D winding number, or Poincaré index. We consider two different applications. We estimate cardiac motion from 3D tagged MRI data, based on tracking of saddle points. We also employ our method for saddle point extraction in blood flow data, acquired by phase contrast MRI.
The thoracic aorta is an anatomical structure that is subject to constant motion. Stent-grafting changes the deformation patterns on the vessel surface, and the nature of these changes is suspected to correlate with treatment outcome. However, they are currently only poorly understood. We propose a method for quantifying the changes in vessel motion caused by stent-grafting in the aortic arch. For...
Rapid acquisition of magnetic resonance (MR) images via reconstruction from undersampled k-space data has the potential to greatly decrease MRI scan time on existing medical hardware. To this end iterative image reconstrction based on the technique of compressed sensing has become the method choice for many researchers [1]. However, while conventional compressed sensing relies on random measurements...
A non-local motion compensation algorithm combined with k-t FOCUSS for high resolution compressed sensing cardiac cine imaging is proposed in this paper. While conventional non-local means algorithms use self-similarity, our method collects similar blocks from another reference frame that is obtained during diastole phase. We show that this non-local motion compensation is optimal in MMSE sense, if...
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