In image-guided interventional procedures, live 2-D X-ray images can be augmented with preoperative 3-D computed tomography or MRI images to provide planning landmarks and enhanced spatial perception. An accurate alignment between the 3-D and 2-D images is a prerequisite for fusion applications. This paper presents a dynamic rigid 2-D/3-D registration framework, which measures the local 3-D-to-2-D misalignment and efficiently constrains the update of both planar and non-planar 3-D rigid transformations using a novel point-to-plane correspondence model. In the simulation evaluation, the proposed method achieved a mean 3-D accuracy of 0.07 mm for the head phantom and 0.05 mm for the thorax phantom using single-view X-ray images. In the evaluation on dynamic motion compensation, our method significantly increases the accuracy comparing with the baseline method. The proposed method is also evaluated on a publicly-available clinical angiogram data set with "gold-standard" registrations. The proposed method achieved a mean 3-D accuracy below 0.8 mm and a mean 2-D accuracy below 0.3 mm using single-view X-ray images. It outperformed the state-of-the-art methods in both accuracy and robustness in single-view registration. The proposed method is intuitive, generic, and suitable for both initial and dynamic registration scenarios.

In interventional radiology, preoperative 3-D volumes can be fused with intra-operative 2-D fluoroscopic images. Since the accuracy is crucial to the clinical usability of image fusion, patient motion resulting in misalignments has to be corrected during the procedure. In this paper, a novel gradient based differential approach is proposed to estimate the 3-D rigid motion from the 2-D tracking of contour points. The mathematical relationship between the 3-D differential motion and the 2-D motion is derived using the 3-D gradient, based on which a tracking-based motion compensation pipeline is introduced. Given the initial registration, the contour points are extracted and tracked along 2-D frames. The 3-D rigid motion is estimated using the iteratively re-weighted least square minimization to enhance the robustness. Our novel approach is evaluated on 10 datasets consisting of 1010 monoplane fluoroscopic images of a thorax phantom with 3-D rigid motion. Over all datasets, the maximum structure shift in the 2-D projection caused by the 3-D motion varies from 17.3 mm to 33.2 mm. Our approach reduces the 2-D structure shift to the range of 1.93 mm to 6.52 mm. For the most challenging longitudinal off-plane rotation, our approach achieves an average coverage of 79.9% regarding to the ground truth.

Massively parallel processor array architectures can be used as hardware accelerators for a plenty of dataflow dominant applications. Bilateral filtering is an example of a state-of-the-art algorithm in medical imaging, which falls in the class of 2D adaptive filter algorithms. In this paper, we propose a semi-automatic mapping methodology for the generation of hardware accelerators for such a generic class of adaptive filtering applications in image processing. The final architecture deliver similar synthesis results as a hand-tuned design.

We focus on the calibration of very long image sequences from a hand-held camera that samples the viewing sphere of a scene. View sphere sampling is important for plenoptic (image-based) modeling that captures the appearance of a scene by storing images from all possible directions. The plenoptic approach is appealing since it allows, in principle, fast scene rendering of scenes with complex geometry and surface reflections, without the need for an explicit geometrical scene model. However the acquired images have to be calibrated, and current approaches mostly use pre-calibrated acquisition systems. This limits the generality of the approach. We propose using an uncalibrated hand-held camera only. The image sequence is acquired by simply waving the camera around the scene objects, creating a zigzag scan path over the viewing sphere. We extend the sequential camera tracking of an existing structure-from-motion approach to the calibration of a mesh of viewpoints. Novel views are generated by piecewise mapping and interpolating the new image from the nearest viewpoints according to the viewpoint mesh. Local depth map estimates enhance the rendering process. Extensive experiments with ground truth data and hand-held sequences confirm the performance of our approach.