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Mutual information (MI) has been widely used in image analysis tasks such as feature selection and image registration. In particular, it is the most widely used similarity measure for intensity based registration of multimodal images. However, a major drawback of MI is that it does not take the spatial neighbourhood into account. An effective way of incorporating spatial information could be of great...
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...
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...
Cone-beam CT images are useful in operative dentistry but suffer from a comparatively bad image quality with regard to the signal-to-noise ratio. Therefore, we use a statistical shape model (SSM) for robust segmentation of the mandible. In contrast to previous approaches, our method (i) is fully automatic in terms of both, the establishment of correspondence and the segmentation itself, and (ii) allows...
Manifold learning techniques have been widely used to produce low-dimensional representations of patient brain magnetic resonance (MR) images. Diagnosis classifiers trained on these coordinates attempt to separate healthy, mild cognitive impairment and Alzheimer's disease patients. The performance of such classifiers can be improved by incorporating clinical data available in most large-scale clinical...
The main focus of this paper is to further improve the performance of the recently introduced higher degree total variation (HDTV) penalties, which are L1-Lp; p ≥ 1 norms of directional image derivatives. We generalize this class as the L1-Lp norms of image responses to rotated versions of an arbitrary derivative operator. We show that several penalties proposed by other researchers are special cases...
This paper investigates the validity of the analytical framework for bias and variance in kinetic parameter estimations. Analytical computation of bias and variance is compared against Monte Carlo simulations for two different compartment models at different noise levels. Difference between the estimated and measured variance increases with the level of noise and complexity of the compartment model...
Fiber tracking techniques are essential for representing and visualizing the cardiac fiber architecture information encoded in diffusion-tensor imaging (DTI) data. We propose a neighborhood-based probabilistic fiber tracking method for cardiac DTI which accounts for spatial correlation and data uncertainty. The method consists in tracking fiber paths by sampling step directions from a normalized weighted...
In PET, as the spatial resolution of the measurement system is increased, multiple interactions of a single photon may be separately measured and such events are often removed from the data used in image reconstruction. While for some PET imaging tasks this effect is unimportant, for primate, brain and high-spatial-resolution imaging where sensitivity is important, such Inter Crystal Scattering (ICS)...
Kinematic analysis of normal and artificial knee joints is an important diagnostic tool for the orthopaedic research community. Abnormal motion in a knee joint gives rise to pain and eventually causes chronic injury. Currently the standard way to measure the motion of bones in a knee joint with high precision is by implanting tantalum beads in the bones prior to imaging using X-ray equipment. This...
Brain imaging is increasingly recognised as an intermediate pheno-type in the understanding of the complex path between genetics and behavioural or clinical phenotypes. In this context, a first goal is to propose methods to identify the part of genetic variability that explains some neuroimaging variability. Here, we investigate multi-variate methods, Partial Least Squares (PLS) regression and Canonical...
Functional imaging serves as an important supplement to anatomical imaging modalities such as MR and CT in modern health care. In perfusion CT (CTP), hemodynamic parameters are derived from the tracking of the first-pass of the contrast bolus entering a tissue region of interest. In practice, however, the post-processed parametric maps tend to be noisy, especially in low-dose CTP, in part due to the...
White matter lesions (WML) are hyperintense signals in T2-weighted MRI of the brain. Volume and regional distribution of WML have been extensively studied in dementia, but not much attention has been given to texture analysis in these regions. We wanted to explore if it is possible to distinguish patients with dementia from healthy elderly in a classification framework testing different texture features...
Thin-plate splines are probably the most often used technique for landmark-based registration with radial basis functions. However, a disadvantage is that the locality of the transformation cannot be controlled. We introduce an extension of thin-plate splines which enables to control the spatial influence of acting forces and thus the locality of the transformation. Our approach is based on a new...
The Metamorphs model is a robust segmentation method which integrates both shape and appearance in a unified space. The standard Metamorphs model does not encode temporal information. Thus it is not effective in segmenting time series data, such as a cardiac cycle from MRI. Furthermore, it needs manual interaction to initialize the model, which is time consuming for temporal data. In this paper, we...
Diffusion tensor imaging (DTI) is sensitive to the directionally-constrained flow of water, which diffuses preferentially along axons. Tractography programs may be used to infer matrices of connectivity (anatomical networks) between pairs of brain regions. Little is known about how these computed connectivity measures depend on the scans' spatial and angular resolutions. To determine this, we scanned...
We propose a novel real-time non central χ (nc-χ) noise correction method for diffusion-weighted MR data that are known to be particularly sensitive to noise, especially at high b-values. This technique aims to be real-time during the acquisition to get any map stemming from the Diffusion Tensor Imaging (DTI) and the High Angular Resolution Diffusion Imaging (HARDI) models corrected from nc-χ noise...
We propose a novel approach for diagnosing malignant lung nodules based on analyzing the spatial distribution of Hounsfield values for the detected lung nodules. Spatial distribution of image intensities (or Hounsfield values) comprising the malignant nodule appearance is accurately modeled with a new rotationally invariant second-order Markov-Gibbs Random Field (MGRF). In this paper, we introduce...
In this paper, we present a new algorithm to register 3D multimodal images with sliding conditions in a diffeomorphic framework. Our driving motivation is to define one-to-one mappings between CT/MR pulmonary volumes acquired from patients with empyema. The main problem to overcome is that the pulmonary motion, which can be large, presents sliding conditions at the thoracic cage boundary. Our algorithm...
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