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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...
In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by...
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...
Registration of ultrasound images is often complicated due to inherent noise. Robust similarity metrics and optimization procedures are required to facilitate medical applicability. In this paper a novel hybrid procedure, incorporating global statistics and local textural features, is proposed for the registration of envelope detected radio frequency ultrasound data. On the global scale this is achieved...
Graph theory can be applied to matrices that represent the brain's anatomical connections, to better understand global properties of anatomical networks, such as their clustering, efficiency and “small-world” topology. Network analysis is popular in adult studies of connectivity, but only one study — in just 30 subjects — has examined how network measures change as the brain develops over this period...
Progress in our understanding of brain functions relies on our capability to explore the human cortical surface at a fine scale (typically 1.5 mm isotropic at 3T). For this purpose, high accuracy is required for all processing steps from image acquisition to data analysis. For group studies, the high intersubject variability of the human cortices hampers their precise registration. Based on the hypothesis...
Lower back pain (LBP) is widely prevalent in people all over the world and negatively affects the quality of life due to chronic pain and change in posture. Automatic localization of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Till date, most of the research has been useful in determining a point within each lumbar disc, hence we...
Magnitude Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) is used to detect lesions in abdominal organs such as the liver. For diagnosis and interventional planning, DW-MRI needs to be registered with Magnetic Resonance (MRI) images in order to provide an anatomical context. DW-MRI usually generates images that contain large regions with only little image information. This makes it difficult...
We introduce an regularized reconstruction scheme to recover dynamic imaging datasets with significant inter frame motion from undersampled Fourier data. The proposed nonlocal regularization penalty is an unweighted sum of distances between image patch pairs in the 3-D dataset. We use robust distance metrics to compute the distance between image patches; these metrics encourage the smoothing between...
Live cell microscopy images enable to study the motion of subcellular particles for understanding biological processes within cells. For accurate quantification of the particle motion, compensation of the motion and deformation of the cell nucleus is required. We introduce a multi-frame non-rigid registration approach for live cell microscopy image sequences. Compared to existing approaches using...
Images collected by intravital microscopy suffer from motion artifacts which necessitates the use of image registration techniques to reconstruct three-dimensional or time series data. In this paper, we describe a multi-resolution/multi-level approach to non-rigid image registration using B-splines with a coarse-to-fine strategy. A control lattice with a coarse grid spacing is used first to account...
Recently we proposed spatio-pathological stratification of lungs from multiple subjects. This enabled a pulmonary disease landscape to objectively diagnose pathology, track progression and assess pharmacologic response within and across patients. Even though the approach based on unsupervised affinity propagation clustering of a symmetric pairwise dissimilarity metric showed strong statistical and...
We propose a novel framework for longitudinal registration which can handle large intra-subject anatomical variations. The framework exploits freely available spatio-temporal atlases, which can aid the longitudinal registration process as it provides prior information about the missing anatomical evolution between two scans taken over large time-interval. The spatio-temporal atlas is used to develop...
More and more researchers are beginning to use multiple dissimilarity metrics or image features for medical image registration. In most of these approaches, however, weights for ranking the relative importance between the selected metrics are empirically tuned and fixed for the entire image domain. Different parts of a medical image, however, may contain significantly different appearance properties...
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