Biomedical subjects

Karl Rohr

Publications and source records attributed to Karl Rohr.

5 recordsLinked to original sources

PubMed2006

Chromatin domains and the interchromatin compartment form structurally defined and functionally interacting nuclear networks.

In spite of strong evidence that the nucleus is a highly organized organelle, a consensus on basic principles of the global nuclear architecture has not so far been achieved. The chromosome territory-interchromatin compartment (CT-IC) model postulates an IC which expands between chromatin domains both in the interior and the periphery of CT. Other models, however, dispute the existence of the IC and claim that numerous chromatin loops expand between and within CTs. The present study was undertaken to resolve these conflicting views. (1) We demonstrate that most chromatin exists in the form of higher-order chromatin domains with a compaction level at least 10 times above the level of extended 30 nm chromatin fibers. A similar compaction level was obtained in a detailed analysis of a particularly gene-dense chromosome region on HSA 11, which often expanded from its CT as a finger-like chromatin protrusion. (2) We further applied an approach which allows the experimental manipulation of both chromatin condensation and the width of IC channels in a fully reversible manner. These experiments, together with electron microscopic observations, demonstrate the existence of the IC as a dynamic, structurally distinct nuclear compartment, which is functionally linked with the chromatin compartment.

Animals↗

PubMed2006

An adaptive irregular grid approach for 3D deformable image registration.

Deformable registration is an important application in medical image analysis and processing. We propose a physics-based parametric approach for deformable image registration, where non-rigid transformations are computed using an irregular grid of control points distributed within the image domain. The image is modelled as a three-dimensional (3D) homogeneous infinite elastic medium. It is assumed that a Gaussian-shaped force is applied at every control point, where the strengths, directions and influence areas of the forces as well as the positions of the control points are considered as free parameters whose optimization leads to maximization of the similarity measure between the images to be registered. For optimization, a computationally efficient Levenberg-Marquardt method is used. The proposed approach has certain advantages over traditional landmark-based methods or the registration methods based on regular grids, for example B-splines, since comparable results can be achieved by using less control points. Experimental results with 3D clinical images demonstrate that our method is capable of successfully coping with complex registration tasks.

Computer Simulation↗

PubMed2006

Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models.

We introduce a new approach for the localization of 3D anatomical point landmarks. This approach is based on 3D parametric intensity models which are directly fitted to 3D images. To efficiently model tip-like, saddle-like, and sphere-like anatomical structures we introduce analytic intensity models based on the Gaussian error function in conjunction with 3D rigid transformations as well as deformations. To select a suitable size of the region-of-interest (ROI) where model fitting is performed, we also propose a new scheme for automatic selection of an optimal 3D ROI size based on the dominant gradient direction. In addition, to achieve a higher level of automation we present an algorithm for automatic initialization of the model parameters. Our approach has been successfully applied to accurately localize anatomical landmarks in 3D synthetic data as well as 3D MR and 3D CT image data. We have also compared the experimental results with the results of a previously proposed 3D differential approach. It turns out that the new approach significantly improves the localization accuracy.

Algorithms↗

PubMed2005

Automatic parameter optimization for de-noising MR data.

This paper describes an automatic parameter optimization method for anisotropic diffusion filters used to de-noise 2D and 3D MR images. The filtering process is integrated into a closed-loop system where image improvement is monitored indirectly by comparing the characteristics of the suppressed noise with those of the assumed noise model at the optimal point. In order to verify the performance of this approach, experimental results obtained with this method are presented together with the results obtained by median and k-nearest neighbor filters.

Algorithms↗

PubMed2003

Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models.

We introduce a new approach for the localization of 3D anatomical point landmarks based on 3D parametric intensity models which are directly fit to the image. We propose an analytic intensity model based on the Gaussian error function in conjunction with 3D rigid transformations as well as deformations to efficiently model tip-like structures of ellipsoidal shape. The approach has been successfully applied to accurately localize anatomical landmarks in 3D MR and 3D CT image data. We have also compared the experimental results with the results of a previously proposed 3D differential operator. It turns out that the new approach significantly improves the localization accuracy.

Algorithms↗