Nonlinear diffusion filtering in image processing is usually performed with explicit schemes. They are only stable for very small time steps, which leads to poor efficiency and limits their practical… (More)
An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation [Cootes and… (More)
Scale-space theory is the theory of apertures, through which we and machines observe the world. The apertures come in an astounding variety. They can be exploited to model the first stages of human… (More)
W h y and how one should study a scale-space is prescribed by the universal physical law of scale invariance, expressed by the so-called Pi-theorem. The fact that any image is a physical observable… (More)
| Nonlinear diiusion ltering is usually performed with explicit schemes. They are only stable for very small time steps, which leads to poor eeciency and limits their practical use. Based on a recent… (More)
Inspired by the visual system of many mammals, we consider the construction of—and reconstruction from—an orientation score of an image, via a wavelet transform corresponding to the left-regular… (More)
OBJECTIVE
Accurate segmentation of lung fields in chest radiographs (CXR) is very useful for automatic analysis of CXR. In this work, we propose to use dense matching of local features and label… (More)
The traditional chest radiograph is still ubiquitous in clinical practice, and will likely remain so for quite some time. Yet, its interpretation is notoriously difficult. This explains the continued… (More)
DBS for Parkinson's disease involves an extensive planning to find a suitable electrode implantation path to the selected target. We have investigated the feasibility of improving the conventional… (More)
Robust and accurate automated co-registration of the coronary arteries in 3D CTA and 2D X-ray angiography during percutaneous coronary interventions (PCI), in order to present a fused visualization.… (More)