Biomedical subjects

Pengcheng Shi

Publications and source records attributed to Pengcheng Shi.

5 recordsLinked to original sources

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

Stochastic inverse consistency in medical image registration.

An essential goal in medical image registration is, the forward and reverse mapping matrices should be inverse to each other, i.e., inverse consistency. Conventional approaches enforce consistency in deterministic fashions, through incorporation of sub-objective cost function to impose source-destination symmetric property during the registration process. Assuming that the initial forward and reverse matching matrices have been computed and used as the inputs to our system, this paper presents a stochastic framework which yields perfect inverse consistency with the simultaneous considerations of the errors underneath the registration matrices and the imperfectness of the consistent constraint. An iterative generalized total least square (GTLS) strategy has been developed such that the inverse consistency is optimally imposed.

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↗

PubMed2003

Meshfree representation and computation: applications to cardiac motion analysis.

For medical image analysis issues where the domain mappings between images involve large geometrical shape changes, such as the cases of nonrigid motion recovery and inter-object image registration, the finite element methods exhibit considerable loss of accuracy when the elements in the mesh become extremely skewed or compressed. Therefore, algorithmically difficult and computationally expensive remeshing procedures must be performed in order to alleviate the problem. We present a general representation and computation framework which is purely based on the sampling nodal points and does not require the construction of mesh structure of the analysis domain. This meshfree strategy can more naturally handle very large object deformation and domain discontinuity problems. Because of its intrinsic h-p adaptivity, the meshfree framework can achieve desired numerical accuracy through adaptive node and polynomial shape function refinement with minimum extra computational expense. We focus on one of the more robust meshfree efforts, the element free Galerkin method, through the moving least square approximation and the Galerkin weak form formulation, and demonstrate its relevancy to medical image analysis problems. Specifically, we show the results of applying this strategy to physically motivated multiframe motion analysis, using synthetic data for accuracy assessment and for comparison to finite element results, and using canine magnetic resonance tagging and phase contrast images for cardiac kinematics recovery.

Algorithms↗

PubMed2003

Stochastic finite element framework for simultaneous estimation of cardiac kinematic functions and material parameters.

A stochastic finite element framework is presented for the simultaneous estimation of the cardiac kinematic functions and material model parameters from periodic medical image sequences. While existing biomechanics studies of the myocardial material constitutive laws have assumed known tissue kinematic measurements, and image analysis efforts on cardiac kinematic functions have relied on fixed constraining models of mathematical or mechanical nature, we illustrate through synthetic data that a probabilistic joint estimation strategy is needed to achieve more robust and accurate analysis of the kinematic functions and material parameters at the same time. For a particular a priori constraining material model with uncertain subject-dependent parameters and a posteriori noisy imaging based observations, our strategy combines the stochastic differential equations of the myocardial dynamics with the finite element method, and the material parameters and the imaging data are treated as random variables with known prior statistics. After the conversion to state space representation, the extended Kalman filtering procedures are adopted to linearize the equations and to provide the joint estimates in an approximate optimal sense. The estimation bias and convergence issues are addressed, and we conclude experimentally that it is possible to adopt this biomechanical model based multiframe estimation approach to achieve converged estimates because of the periodic nature of the cardiac dynamics. The effort is validated using synthetic data sequence with known kinematics and material parameters. Further, under linear elastic material model, estimation results using canine magnetic resonance phase contrast image sequences are presented, which are in very good agreement with histological tissue staining results, the current gold standards.

Animals↗