Biomedical subjects

Hamid Krim

Publications and source records attributed to Hamid Krim.

5 recordsLinked to original sources

PubMed2006

An independent component analysis approach for minimizing effects of recirculation in dynamic susceptibility contrast magnetic resonance imaging.

In dynamic susceptibility contrast (DSC) perfusion-weighted imaging, effects of recirculation are normally minimized by a gamma-variate fitting procedure of the concentration curves before estimating hemodynamic parameters. The success of this method, however, hinges largely on the extent to which magnetic resonance signal is altered in the presence of a contrast agent and a temporal separation between the first and subsequent passages of the contrast agent. Moreover, important physiologic information might be compromised by imposing an analytic equation to all measured concentration curves. This investigation proposes to exploit independent component analysis to minimize effects of recirculation in DSC. Results obtained from simulation, normal healthy volunteers, and acute stroke patients show that such a technique can greatly minimize the effects of recirculation despite a substantial overlap between the first passage and recirculation. This in turn should improve estimation of cerebral hemodynamics particularly when an overlap between the first passage and recirculation is suspected as in an ischemic lesion.

Adult↗

PubMed2006

Geodesic matching of triangulated surfaces.

Recognition of images and shapes has long been the central theme of computer vision. Its importance is increasing rapidly in the field of computer graphics and multimedia communication because it is difficult to process information efficiently without its recognition. In this paper, we propose a new approach for object matching based on a global geodesic measure. The key idea behind our methodology is to represent an object by a probabilistic shape descriptor that measures the global geodesic distance between two arbitrary points on the surface of an object. In contrast to the Euclidean distance which is more suitable for linear spaces, the geodesic distance has the advantage to be able to capture the intrinsic geometric structure of the data. The matching task therefore becomes a one-dimensional comparison problem between probability distributions which is clearly much simpler than comparing three-dimensional structures. Object matching can then be carried out by an information-theoretic dissimilarity measure calculations between geodesic shape distributions, and is additionally computationally efficient and inexpensive.

Algorithms↗

PubMed2005

Fast incorporation of optical flow into active polygons.

In this paper, we first reconsider, in a different light, the addition of a prediction step to active contour-based visual tracking using an optical flow and clarify the local computation of the latter along the boundaries of continuous active contours with appropriate regularizers. We subsequently detail our contribution of computing an optical flow-based prediction step directly from the parameters of an active polygon, and of exploiting it in object tracking. This is in contrast to an explicitly separate computation of the optical flow and its ad hoc application. It also provides an inherent regularization effect resulting from integrating measurements along polygon edges. As a result, we completely avoid the need of adding ad hoc regularizing terms to the optical flow computations, and the inevitably arbitrary associated weighting parameters. This direct integration of optical flow into the active polygon framework distinguishes this technique from most previous contour-based approaches, where regularization terms are theoretically, as well as practically, essential. The greater robustness and speed due to a reduced number of parameters of this technique are additional and appealing features.

Algorithms↗

PubMed2005

Identification of a discrete planar symmetric shape from a single noisy view.

In this paper, we propose a method for identifying a discrete planar symmetric shape from an arbitrary viewpoint. Our algorithm is based on a newly proposed notion of a view's skeleton. We show that this concept yields projective invariants which facilitate the identification procedure. It is, furthermore, shown that the proposed method may be extended to the case of noisy data to yield an optimal estimate of a shape in question. Substantiating examples are provided.

Algorithms↗

PubMed2004

Smart nonlinear diffusion: a probabilistic approach.

In this paper, a stochastic interpretation of nonlinear diffusion equations used for image filtering is proposed. This is achieved by relating the problem of evolving/smoothing images to that of tracking the transition probability density functions of an underlying random process. We show that such an interpretation of, e.g., Perona-Malik equation, in turn allows additional insight and sufficient flexibility to further investigate some outstanding problems of nonlinear diffusion techniques. In particular, upon unraveling the limitations as well as the advantages of such an equation, we are able to propose a new approach which is demonstrated to improve performance over existing approaches and, more importantly, to lift the longstanding problem of a stopping criterion for a nonlinear evolution equation with no data term constraint. Substantiating examples in image enhancement and segmentation are provided.

Algorithms↗