Biomedical subjects

Yunmei Chen

Publications and source records attributed to Yunmei Chen.

5 recordsLinked to original sources

PubMed2005

Application of partial differential equation-based inpainting on sensitivity maps.

Inpainting is an image interpolation method. Partial differential equation (PDE)-based digital inpainting techniques are finding broad applications. In this paper, PDE-based inpainting techniques are applied to the field of MR parallel imaging. A novel model and its corresponding numerical method are introduced. This model is then applied to sensitivity maps. Coil sensitivity maps are important for parallel imaging, and they often require extrapolation and hole filling (holes being dark regions of low signal in MR images). These problems can be solved simultaneously by the application of inpainting techniques. Experiments for determining coil sensitivity maps for phantoms and cardiac MR images demonstrate the accuracy of the proposed model. Images generated using sensitivity encoding (SENSE) that utilizes inpainted sensitivity maps, thin-plate spline (TPS) estimated sensitivity maps, and Gaussian kernel smoothed (GKS) sensitivity maps are compared. From the experimental results, it can be seen that inpainted sensitivity maps produce better results than GKS sensitivity maps. The TPS method generates results similar to those of the inpainting technique but is much more time-consuming.

Algorithms↗

PubMed2005

Point-based geometric deformable models for medical image segmentation.

Conventional level set based image segmentations are performed upon certain underlying grid/mesh structures for explicit spatial discretization of the problem and evolution domains. Such computational grids, however, lead to typically expensive and difficult grid refinement/remeshing problems whenever tradeoffs between time and precision are deemed necessary. In this paper, we present the idea of performing level set evolution in a point-based environment where the sampling location and density of the domains are adaptively determined by level set geometry and image information, thus rid of the needs for computational grids and the associated refinements. We have implemented the general geometric deformable models using this representation and computational strategy, including the incorporation of region-based prior information in both domain sampling and curve evolution processes, and have evaluated the performance of the method on synthetic data with ground truth and performed surface segmentation of brain structures from three-dimensional magnetic resonance images.

Algorithms↗

PubMed2005

A variational PDE based level set method for a simultaneous segmentation and non-rigid registration.

A new variational PDE based level set method for a simultaneous image segmentation and non-rigid registration using prior shape and intensity information is presented. The segmentation is obtained by finding a non-rigid registration to the prior shape. The non-rigid registration consists of both a global rigid transformation and a local non-rigid deformation. In this model, a prior shape is used as an initial contour which leads to decrease the numerical calculation time. The model is tested against two chamber end systolic ultrasound images from thirteen human patients. The experimental results provide preliminary evidence of the effectiveness of the model in detecting the boundaries of the incompletely resolved objects which were plagued by noise, dropout, and artifact.

Algorithms↗

PubMed2005

Geodesic active contours with adaptive neighboring influence.

While geometric deformable models have brought tremendous impacts on shape representation and analysis in medical image analysis, some of the remaining problems include the handling of boundary leakage and the lack of global understanding of boundaries. We present a modification to the geodesic active contour framework such that influence from local neighbors of a front point is explicitly incorporated, and it is thus capable of robustly dealing with the boundary leakage problem. The fundamental power of this strategy rests with the local integration of evolution forces for each front point within its local influence domain, which is adaptively determined by the local level set geometry and image/prior information. Due to the combined effects of internal and external constraints on a point and the interactions with those of its neighbors, our method allows stable boundary detection when the edge information is noisy and possibly discontinuous (e.g. gaps in the boundaries) while maintaining the abilities to handle topological changes, thanks to the level set implementation. The algorithm has been implemented using the meshfree particle domain representation, and experimental results on synthetic and real images demonstrate its superior performance.

Algorithms↗

PubMed2004

A constrained variational principle for direct estimation and smoothing of the diffusion tensor field from complex DWI.

In this paper, we present a novel constrained variational principle for simultaneous smoothing and estimation of the diffusion tensor field from complex valued diffusion-weighted images (DWI). The constrained variational principle involves the minimization of a regularization term of L(P) norms, subject to a nonlinear inequality constraint on the data. The data term we employ is the original Stejskal-Tanner equation instead of the linearized version usually employed in literature. The complex valued nonlinear form leads to a more accurate (when compared to the linearized version) estimate of the tensor field. The inequality constraint requires that the nonlinear least squares data term be bounded from above by a known tolerance factor. Finally, in order to accommodate the positive definite constraint on the diffusion tensor, it is expressed in terms of Cholesky factors and estimated. The constrained variational principle is solved using the augmented Lagrangian technique in conjunction with the limited memory quasi-Newton method. Experiments with complex-valued synthetic and real data are shown to depict the performance of our tensor field estimation and smoothing algorithm.

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