A new, fully automated method for non-rigid registration of multimodal images is presented. Due to the large interdependance of segmentation and registration, the approach is based on simultaneous segmentation and edge-alignment. The two processes are directly coupled and thus benefits from using complementary information of the entire underlying dataset. It is formulated as a variational joint...

Multimodality image registration and fusion are essential steps in building 3-D models from remote sensing data. In this paper, we present a neural network technique for the registration and fusion of multimodality remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network is used to fuse the intensity data sets with the spatial data set after lear...

Image registration is the process of aligning two or more images of the same scene taken at different times; from different viewpoints; and/or by different sensors. This research focuses on developing a practical method for automatic image registration for agricultural systems that use multimodal sensory systems and operate in natural environments. While not limited to any particular modalities...

Mutual information (MI) was introduced for use in multimodal image registration over a decade ago [1,2,3,4]. The MI between two images is based on their marginal and joint/conditional entropies. The most common versions of entropy used to compute MI are the Shannon and differential entropies; however, many other definitions of entropy have been proposed as competitors. In this article, we show ...

PURPOSE In this paper, the method for the creation of an anatomically and mechanically realistic brain phantom from polyvinyl alcohol cryogel (PVA-C) is proposed for validation of image processing methods such as segmentation, reconstruction, registration, and denoising. PVA-C is material widely used in medical imaging phantoms because of its mechanical similarities to soft tissues. METHODS T...

Mutual information is an attractive registration criterion because it provides a meaningful comparison of images that represent different physical properties. In this paper, we review the shortcomings of three published methods for its computation. We identify the grid effect and the overlap problem as the most severe artifacts that these methods face, and propose a solution based on irregular ...

This paper deals with the correction of distortions in EPI acquisitions. Echo-planar imaging (EPI) data is used in functional resonance imaging (fMRI) and in diffusion tensor MRI (dMRI) because of its impressive ability to collect data rapidly. However, these data contain geometrical distortions that degrade the quality of the scans and disturb their interpretation. In this paper, we present a ...

Image Registration is a central task to many medical image analysis applications. In this paper, we present a novel iterative algorithm composed of two main steps: a global affine image registration based on particle filter, and a local refinement obtained from a linear optical flow approximation. The key idea is to iteratively apply these simple and robust steps to efficiently solve complex no...

For the problem of multimodal image registration, an optimal control approach is presented. The geometrical information of the images will be transformed into weighted edge sketches, for which a linear-elastic or hyperelastic registration will be performed. For the numerical solution of this problem, we provide a direct method based on discretization methods and large-scale optimization techniq...

In this paper, a nonrigid registration method is presented for accommodating local shape variation when matching monomodal images or multimodal images. Affine transformation is adopted in global registration while local deformation is described by a free-form deformation based on a linear singular blending (LSB) B-spline, which can enhance the shape-control capability of the B-spline. This capa...