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The registration of breast DCE-MR images can help correct possible motions during image acquisition, and is also important for diagnosis of breast cancer, i.e., discrimination between benign and malignant tumors. However, deformable registration of DCE-MR images is challenging due to drastic image contrast change over time (especially between pre- and post-contrast images). To improve the registration,...
Since the visual quality of an infrared (IR) image is usually unsatisfactory due to blurred edges and lack of textures, it is sometimes hard to obtain sufficient information from the IR image. In this paper, we present a novel framework for the IR image enhancement with the help of its aligned high resolution visible image. In the algorithm, we first prepare an aligned pair of IR and visible images...
This paper extends a recent image-dependent regularisation approach introduced in aiming at edge-preserving smoothing. For that purpose, geodesic distances equipped with a Riemannian metric need to be estimated in local neighbourhoods. By deriving an appropriate metric from the gradient structure tensor, the associated geodesic paths are constrained to follow salient features in images. Following,...
Image registration is an indispensable process in the detection of brain structural and anatomical abnormities. Inverse-consistency, topology preserving and real time application are essential to provide accurate deformation fields for statistical analysis of brain variability. Unfortunately, the previous algorithms lacked of these features. We present a registration method by adapting the optimization...
2D or 3D dataset transformations, interpolations and resampling operations are essential techniques that are able to display graphical models in several fields such as medical visualization and engineering. In this study, an overview of interpolation approaches of the last ten years was given and a resampling application for magnetic resonance images was implemented using free form cubic b-spline...
In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by...
Compensating for cardio-thoracic motion artifacts in contrast-enhanced cardiac perfusion MRI (p-MRI) sequences is a key issue for the quantitative assessment of myocardial iscæmia. The classical paradigm consists of registering each sequence frame on some reference using an intensity-based matching criterion. In this paper, we present a novel unsupervised method for the groupwise registration of cardiac...
The criterion for the correct spatial alignment is a key component in image registration. We formulate the registration problem as one that finds the spatial and intensity mappings of minimal complexity that make images exactly equal. We do not assume any parametric forms of these functions, and estimate them within variational calculus. We analytically solve for non-stationary intensity mapping,...
This paper proposes techniques for accelerating a software based image registration algorithm for 3D medical images targeting a reconfigurable hardware platform. Various methods, including dedicated fixed point arithmetic, error model based bit width analysis, architecture exploration and application-specific memory modules, are applied to address issues from the software algorithm and to maximize...
We propose an extension of mutual information and a new fast algorithm to accelerate the evaluation of mutual information of images. This algorithm adopts gauss function as kernel function, and then uses fast gauss transform to reduce time complexity and improves the fast gauss transform by adaptive k-center clustering. The new algorithm can estimate smoother curves of mutual information function...
The local color distribution centered on feature point is usually used for image registration, in which the estimation for transformational parameters is the core problem. As the reported methods can not suit for large image deformations and are on the assumption that transformational parameters changes are followed by linear or Gaussian distribution so that the estimation process is easy to get into...
Parameterized appearance models (PAMs) (e.g. eigen-tracking, active appearance models, morphable models) use principal component analysis (PCA) to model the shape and appearance of objects in images. Given a new image with an unknown appearance/shape configuration, PAMs can detect and track the object by optimizing the modelpsilas parameters that best match the image. While PAMs have numerous advantages...
Mutual information (MI) has proven its effectiveness for automated multimodal image registration for numerous remote sensing applications like image fusion. We analyze MI performance with respect to joint histogram bin size and the employed joint histogramming technique. The affect of generalized partial volume estimation (GPVE) utilizing B-spline kernels with different histogram bin sizes on MI performance...
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