Biomedical subjects

Pheng Ann Heng

Publications and source records attributed to Pheng Ann Heng.

7 recordsLinked to original sources

PubMed2006

Shape statistics variational approach for the outer contour segmentation of left ventricle MR images.

Segmentation of left ventricles is one of the important research topics in cardiac magnetic resonance (MR) imaging. The segmentation precision influences the authenticity of ventricular motion reconstruction. In left ventricle MR images, the weak and broken boundary increases the difficulty of segmenting the outer contour precisely. In this paper, we present an improved shape statistics variational approach for the outer contour segmentation of left ventricle MR images. We use the Mumford-Shah model in an object feature space and incorporate the shape statistics and an edge image to the variational framework. The introduction of shape statistics can improve the segmentation with broken boundaries. The edge image can enhance the weak boundary and thus improve the segmentation precision. The generation of the object feature image, which has homogenous "intensities" in the left ventricle, facilitates the application of the Mumford-Shah model. A comparison of mean absolute distance analysis between different contours generated with our algorithm and that generated by hand demonstrated that our method can achieve a higher segmentation precision and a better stability than various approaches. It is a semiautomatic way for the segmentation of the outer contour of the left ventricle in clinical applications.

Algorithms↗

PubMed2006

Semi-automatic segmentation and tracking of CVH data.

Construction of speed function is crucial in applying level set method for medical image segmentation. We present a unified approach for segmenting and tracking of the high-resolution Chinese Visible Human (CVH) data. The underlying link of these two parts relies on the proposed variational framework for the speed function. Our proposed method can be applied to segmenting the first slice of the volume data, in the first step; It can also be adapted to track the boundaries of the homogeneous organs in the following serial images. In addition to promising segmentation results, the tracking procedure shows the advantage of less amount of user intervention.

Algorithms↗

PubMed2005

LV shape and motion: B-spline-based deformable model and sequential motion decomposition.

In this paper, we extend a previous work by J. Park and propose a uniform framework to reconstruct left ventricle (LV) geometry/motion from tagged MR images. In our work, the LV is modeled as a generalized prolate spheroid, and its motion is decomposed into four components-global translation, polar radial/z-axis compression, twisting, and bending. By formulating model parameters as tensor products of B-splines, we develop efficient algorithms to quickly reconstruct LV geometry/motion from extracted boundary contours and tracked planar tags. Experiments on both synthesized and in vivo data are also reported.

Algorithms↗

PubMed2005

Cardiac MR image segmentation and left ventricle surface reconstruction based on level set method.

A two-stage segmentation algorithm is presented to solve the problems of inhomogeneity, weak edges and artifacts exhibited in the magnetic resonance imaging (MRI) images. First, the K-mean clustering algorithm is applied to classify the objects. Then, a speed function based on the clustering results is defined in order to search the rough boundary. Secondly, a speed function of the gradient intensity is constructed to locate the boundary accurately. Due to the lack of deformation information of the boundaries between MR slices, a deformable model is used to reconstruct the shape of the LV: a dynamic equation governing the surface deformation is given; from the slice data, external forces are constructed and elastic forces are provided with mean curvatures of the deformation surface. The level set method is applied to solve the dynamic equation for the LV shape. Experimental results demonstrate the effectiveness of the algorithm listed in the paper.

Algorithms↗

PubMed2003

A comparison of truncated total least squares with Tikhonov regularization in imaging by ultrasound inverse scattering.

For good image quality using ultrasound inverse scattering, one alternately solves the well-posed forward scattering equation for an estimated total field and the ill-posed inverse scattering equation for the desired object property function. In estimating the total field, error or noise contaminates the coefficients of both matrix and data of the inverse scattering equation. Previous work on ill-posed inverse ultrasonic scattering commonly used Tikhonov regularization, which considers error only in the data. The solution so obtained is not precise enough to reconstruct the quantitative internal structure of a large or high-contrast object. This paper adopts the truncated total least squares method, simultaneously considering error and noise on both sides of the inverse scattering equation, and compares it with the classical Tikhonov regularization. We show that it can substantially improve reconstruction fit and image quality when the inverse scattering equation system is strongly ill-posed.

Algorithms↗

PubMed2003

A hybrid condensed finite element model for interactive 3D soft tissue cutting.

As requested from practical operations, it is necessary in the design to provide the ability to real-time cut and suture the tissue in a surgery simulation apart from the deformation simulation. In this paper, we present a deformation model, referred as the hybrid condensed FE model, based on the volumetric finite element method. The most important advantage of this model is its ability to handle the topology change freely with interactive rate in the surgical simulation on current PC platform.

Computer Simulation↗

PubMed2002

Vector entropy imaging theory with application to computerized tomography.

Medical imaging theory for x-ray CT and PET is based on image reconstruction from projections. In this paper a novel vector entropy imaging theory under the framework of multiple criteria decision making is presented. We also study the most frequently used image reconstruction methods, namely, least square, maximum entropy, and filtered back-projection methods under the framework, of the single performance criterion optimization. Finally, we introduce some of the results obtained by various reconstruction algorithms using computer-generated noisy projection data from the Hoffman phantom and real CT scanner data. Comparison of the reconstructed images indicates that the vector entropy method gives the best in error (difference between the original phantom data and reconstruction), smoothness (suppression of noise), grey value resolution and is free of ghost images.

Algorithms↗