Showing papers by "Chun-Hsiung Fang published in 2009"

3D Volumetric Visualization with Automatic Rigid and Deformable Hybrid Image Registration for Adaptive Radiotherapy

Abstract: To provide more clinically convenient image fusions for adaptive radiotherapy (ART), an automatic rigid and deformable image registration framework (AIRF) is developed for multimodal visualization of multiple chronological images and multiple radiotherapy (RT) plans Our hybrid image registration framework, AIRF, uses a faster but less accurate rigid registration method to provide an initial registration, followed by a slower but more accurate deformable registration method to fine tune the final registration A multi-resolution approach is also employed in the image registration process to further improve the registration accuracy, robustness and efficiency Volume visualization is provided to guide the automatic image registration process because it can reduce the global positioning error that results from a partial 3D visual presentation in the three conventional orthogonal planar views (axial, sagittal, and coronal) The AIRF can automatically align multiple volumetric images of patients taken over an extended period of time and can merge multiple radiotherapy plans based on different planning computed tomography (CT) images It offers illustrative 3D volumetric visualization, hybrid rigid and deformable image registration, and automatic transfer of RT dose distribution and RT structure models such as treatment targets and organs at risk (OARs) onto chronological images The AIRF can automatically register multiple volumetric image datasets of patients taken over an extended period of time and can merge multiple RT plans based on different planning CT images for 4D or adaptive radiotherapy

Quantitative Coronary Analysis Medical Image Processing Improved by Combining Wavelet Edge Detection and Segmentation

Abstract: The Quantitative Coronary Analysis (QCA) is a useful method for physicians to diagnose heart artery stenosis. So the precision of coronary angiography image quality is very definite important. In this study we present a precision 2-D image processing method that combines gradient segmentation and region segmentation approaches with an entropy maximization procedure. This method allows us to utilize all available information to achieve the most robust segmentation results for coronary angiography analysis. The aim is to improve the methods of 2-D coronary angiography image processing those usually lack such as precision. The example of coronary angiography data of true medical images were used to test the validity of our method. We found that our method not only achieved the precision we sought but also has many interesting applications that shall be most useful in medical practices.

A Volume Visualization System with Augmented Reality Interaction for Evaluation of Radiotherapy Plans

Abstract: The introduction of adaptive image-guided radiotherapy (ART) has contributed to the rapid accumulation of spatiotemporal medical image data, often making it difficult to consolidate information on a single patient. For oncology patients, the lack of data integration can negatively impact on patient care. We developed a volume visualization system with augmented reality interaction to display radiotherapy plan contents including computed tomography (CT) images, dose distribution, and mesh models of radiotherapy targets. This system also provided quantitative indices about the radiotherapy dose coverage based on composite measurement of surface distance and volume difference between target volume and the volume of multiple isodose levels. The dose coverage data of a radiotherapy plan could be incorporated as feedback for deriving "accept or reject" decisions to improve the quality of radiotherapy on an individual patient basis using the surface distance comparison methods for different volumes.

Improvements on stabilization and robust tracking control for T-S fuzzy control systems

Abstract: By using the non-PDC control and the multiple Lyapunov function, a set of LMI-based stabilization conditions for continuous-time T-S fuzzy systems is developed in the paper. In comparison with existing results, not only the derived condition is more relaxed but the approach used is simpler and more realizable in dealing with the time derivatives of membership functions. Some drawback existing in current literature is removed in the paper. The proposed idea is also extended to solve the robust tracking control problem and obtain a more satisfactory tracking performance. At the end, practical examples are given to illustrate the effectiveness of the proposed approach.

A multimodality image registration framework for synchronous visualization of radiotherapy plans with longitudinal imaging studies

Abstract: The introduction of adaptive image-guided radiotherapy (IGRT) in the radiotherapy environment has contributed to the rapid accumulation of medical image data, often making it difficult to consolidate information on a single patient. For oncology patients, the lack of data integration can negatively impact on patient care. We propose a multimodality image registration framework (MIRF) for radiotherapy plans to be registered automatically with longitudinal follow-up images. Volume visualization is proved to display changes in serial medical images, and transformed dose distribution and mesh models of radiotherapy targets can be superimposed on these successive images.

Fuzzy tracking control design

Abstract: The tracking control problem of T-S fuzzy systems is solved in the paper. A new method is proposed to improve the existing result. All the designed conditions are expressed in the form of LMIs. Thus they are numerically realizable. From the simulation example, it can be seen that the proposed approach achieves a better tracking performance.

An improvement on observer-based H ∞ control of fuzzy systems

Abstract: The paper solves the observer-based H ∞ control problems of T-S fuzzy systems. Two conditions that guarantee the existence of H ∞ controller based on fuzzy observers are developed. The conditions are more relaxed than the existing one and ensure the designed fuzzy controller achieving a better H ∞ control performance. A numerical example is given to demonstrate the validity and applicability of the proposed approach in the control of a nonlinear system.

Stabilization of discrete-time nonlinear control systems - Multiple fuzzy Lyapunov function approach

Abstract: This paper deals with the stabilization problem for discrete-time nonlinear systems that are represented by the Takagi -Sugeno fuzzy model. By the multiple fuzzy Lyapunov function and the three-index algebraic combination technique, a new stabilization condition is developed. The condition is expressed in the form of linear matrix inequalities (LMIs) and proved to be less conservative than existing results in the literature. Finally, a truck-trailer system is given to illustrate the novelty of the proposed approach.