2728620
Mixture-based bone segmentation and its application in computer aided diagnosis and treatment planning
The developmental dysplasia of the hip (DDH) is a congenital malformation of the acetabulum with different degrees. Leaving it partially treated or untreated is a major cause of disability. In this paper, we implemented a computer-aided system for the diagnosis and treatment planning of DDH. Instead of labeling each voxel with a unique label or tissue type, the percentage of different tissues within each voxel, which we call a mixture, was considered in establishing a maximum a posterior probability (MAP) framework for more accurate segmentation. A graphical module displayed the segmented bone in a 3D view to show the spatial relationship between acetabulum and femurs. This virtual image, together with quantitative measurement indexes, helps physicians make diagnosis. The system also provides physicians a virtual treatment planning on screen for acetabular and femoral osteotomy. It is currently under clinical trial on patient datasets.
11362746
A novel approach to extract colon lumen from CT images for virtual colonoscopy
An automatic method has been developed for segmentation of abdominal computed tomography (CT) images for virtual colonoscopy obtained after a bowel preparation of a low-residue diet with ingested contrast solutions to enhance the image intensities of residual colonic materials. Removal of the enhanced materials was performed electronically by a computer algorithm. The method is a multistage approach that employs a modified self-adaptive on-line, vector quantization technique for a low-level image classification and utilizes a region-growing strategy for a high-level feature extraction. The low-level classification labels each voxel based on statistical analysis of its three-dimensional intensity vectors consisting of nearby voxels. The high-level processing extracts the labeled stool, fluid and air voxels within the colon, and eliminates bone and lung voxels which have similar image intensities as the enhanced materials and air, but are physically separated from the colon. This method was evaluated by volunteer studies based on both objective and subjective criteria. The validation demonstrated that the method has a high reproducibility and repeatability and a small error due to partial volume effect. As a result of this electronic colon cleansing, routine physical bowel cleansing prior to virtual colonoscopy may not be necessary.
10908543
Construct Teacher Community in E-Learning System
At present, most universities had completed the basic facilities and services of Information and Communication Technology (ICT), and installed e-learning system. What should be done to facilitate teachers' transition to employ ICT to their new teaching practices? We believe that virtual communities for Teacher Professional development (TPD) and socialization are supposed to be an excellent solution. We also deem that e-learning system could be a place for TPD as well as assisting teaching. In this paper, we present our past attempts, and the rationale behind the design of the Zjiao.Com virtual community.
38198465
Control of a 3-dimensional snake-like robot
Biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. The special structure of snakes and their unique movement offer them peculiar ability of climbing and moving even in some ill-conditioned environments such as on the marshland or in narrow tubes. In this paper, we first discuss the controllability of a 3-dimensional snake-like robot, and then show the controllability of the robot by computer simulation.
35611392
CLUCGS and CLUCR - two matrix solution methods for general circuit simulation
Direct methods and iterative methods have been used in most analog circuit analysis tools for matrix solution. But neither can efficiently solve the large-scale sparse matrix or ill matrix because of the closely coupled relationship of execution time and matrix size for direct methods and the convergence problem for iterative methods. This paper presents two methods that are a combination of direct solution using complete LU factorization and iterative solution using conjugate gradient square (CGS) or conjugate residual (CR) method. The methods converge faster and more reliably than the LU factorization method and conjugate gradient (CG) method, and can achieve high resolution on a wide variety of circuits. A very efficient preconditioning method is used in our approach to accelerate convergence. Furthermore, we present an effective judgment method for re-factorization of matrix. Several numerical experiments results are included to compare their efficiencies.
29843270
A Self-adaptive Mixed Distribution Based Uni-variate Estimation of Distribution Algorithm for Large Scale Global Optimization
Large scale global optimization (LSGO), which is highly needed for many scientific and engineering applications, is a very important and very difficult task in optimization domain. Various algorithms have been proposed to tackle this challenging problem, but the use of estimation of distribution algorithms (EDAs) to it is rare. This chapter aims at investigating the behavior and performances of uni-variate EDAs mixed with different kernel probability densities via fitness landscape analysis. Based on the analysis, a self-adaptive uni-variate EDA with mixed kernels (MUEDA) is proposed. To assess the effectiveness and efficiency of MUEDA, function optimization tasks with dimension scaling from 30 to 1500 are adopted. Compared to the recently published LSGO algorithms, MUEDA shows excellent convergence speed, final solution quality and dimensional scalability.
16012768
Continuous Optimization based-on Boosting Gaussian Mixture Model
A new estimation of distribution algorithm (EDA) based on Gaussian mixture model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A set of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions