Biomedical subjects

Vladimir Pekar

Publications and source records attributed to Vladimir Pekar.

4 recordsLinked to original sources

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↗

PubMed2004

Automated model-based organ delineation for radiotherapy planning in prostatic region.

PURPOSE: Organ delineation is one of the most tedious and time-consuming parts of radiotherapy planning. It is usually performed by manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional (3D) data set used for planning. In this paper, a 3D model-based approach to automated organ delineation is introduced that allows for a significant reduction of the time required for contouring. METHODS AND MATERIALS: The presented method is based on an adaptation of 3D deformable surface models to the boundaries of the anatomic structures of interest. The adaptation is based on a tradeoff between deformations of the model induced by its attraction to certain image features and the shape integrity of the model. To make the concept clinically feasible, interactive tools are introduced that allow quick correction in problematic areas in which the automated model adaptation may fail. A feasibility study with 40 clinical data sets was done for the male pelvic area, in which the risk organs (bladder, rectum, and femoral heads) were segmented by automatically adapting the corresponding organ models. RESULTS: In several cases of the validation study, minor user interaction was required. Nevertheless, a statistically significant reduction in the time required compared with manual organ contouring was achieved. The results of the validation study showed that the presented model-based approach is accurate (1.0-1.7 mm mean error) for the tested anatomic structures. CONCLUSION: A framework for organ delineation in radiotherapy planning is presented, including automated 3D model-based segmentation, as well as tools for interactive corrections. We demonstrated that the proposed approach is significantly more efficient than manual contouring in two-dimensional slices.

Algorithms↗

PubMed2004

Automated segmentation of the left ventricle in cardiac MRI.

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation.

Algorithms↗

PubMed2003

Automated 3-D PDM construction from segmented images using deformable models.

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.

Algorithms↗