Optical flow models are widely used for different image registration applications due to their accuracy and fast computation. Major disadvantages to overcome for medical image registration are large deformations and inaccurate regularisation at discontinuities, which cannot be modelled accurately with quadratic regularisers, and an intensity dependent energy term, which does not allow for image...

This paper introduces a new similarity measure for multimodal image registration task. The measure is based on the generalized survival exponential entropy (GSEE) and mutual information (GSEE-MI). Since GSEE is estimated from the cumulative distribution function instead of the density function, it is observed that the interpolation artifact is reduced. The method has been tested on four real MR...

In this paper, we propose a nonrigid image registration method using a Markov Random Field (MRF) energy model with higher-order smoothness priors and a dense local descriptor. The image registration is designed as finding an optimal labeling of the MRF energy model where the label corresponds to a discrete displacement vector. The proposed MRF energy model uses matching scores of dense local de...

In this work, thirteen image-pairs, composed of a color fundus photograph and a scanning laser ophthalmoscope fluorescein angiography (SLO.FA), were registered by rigid, non-rigid and deformable image registration procedures to assess the need for deformable image registration methods when using SLO.FAmodalities. Each registration was quantitatively assessed by three different metrics that show...

In this paper we propose solutions that allow several co-located city planners to perform domain-specific interaction tasks together in one semi-immersive projection-based VR system. After a registration process, e.g., by posing gestures or by using speech commands, this group activity can be performed either in a cooperative mode, i.e., tasks are accomplished consecutively, or in a collaborati...

In this paper we present a new method for the registration of multiple sensors applied to a mobile robotic inspection platform. Our main technical challenge is automating the integration process for various multimodal inputs, such as depth maps, and multi-spectral images. This task is approached through a unified framework based on a new registration criterion that can be employed for both 3D a...

This paper deals with a new approach of registration in multimodal imaging. Modalities involved are Digital Subtracted Angiography (DSA, 2D) and Magnetic Resonance Angiography (MRA, 3D). Our approach is an hybrid one, mixing feature and intensity based approaches. This approach is based on the extraction of a anatomical referential common to both MRA and DSA. This step appears to be the “geomet...

BACKGROUND This paper presents a method that registers MRIs acquired in prone position, with surface topography (TP) and X-ray reconstructions acquired in standing position, in order to obtain a 3D representation of a human torso incorporating the external surface, bone structures, and soft tissues. METHODS TP and X-ray data are registered using landmarks. Bone structures are used to register...

This paper proposes a new intensity-based similarity metric that can be used for the registration of multimodal images. It combines the robust estimation with both the forward and inverse transformation to reduce the negative effects of outliers in the images. For this purpose, we firstly employ the multiresolution technique to downsample the original images, then resort to the simulated anneal...

Joint histogram of two images is required to uniquely determine the mutual information between the two images. It has been pointed out that, under certain conditions, existing joint histogram estimation algorithms like partial volume interpolation (PVI) and linear interpolation may result in different types of artifact patterns in the MI based registration function by introducing spurious maxim...