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OBJECTIVES To characterize the brain activation patterns evoked by manual and electroacupuncture on normal human subjects. DESIGN We used functional magnetic resonance imaging (fMRI) to investigate the brain regions involved in electroacupuncture and manual acupuncture needle stimulation. A block design was adopted for the study. Each functional run(More)
In this paper, an automatic and robust coarse-to-fine liver image segmentation method is proposed. Multiple prior knowledge models are built to implement liver localization and seg-mentation: voxel-based AdaBoost classifier is trained to localize liver position robustly, shape and appearance models are constructed to fit liver shape and appearance models to(More)
In this contribution, we develop a novel global threshold-based active contour model. This model deploys a new edge-stopping function to control the direction of the evolution and to stop the evolving contour at weak or blurred edges. An implementation of the model requires the use of selective binary and Gaussian filtering regularized level set (SBGFRLS)(More)
Neuronal recording and neuroimaging studies have shown that the primary motor area (M1) not only participates in motor execution, but is also engaged during movement preparation. The purpose of the present study was to map the distribution of the preparation- and execution-related activity within the contralateral M1 using functional magnetic resonance(More)
In recent years, research on human functional brain imaging using resting-state fMRI techniques has been increasingly prevalent. The term “default mode” was proposed to describe a baseline or default state of the brain during rest. Recent studies suggested that the default mode network (DMN) is comprised of two functionally distinct subsystems: a(More)
Efficient visualization of vascular structures is essential for therapy planning and medical education. Existing techniques achieve high-quality visualization of vascular surfaces at the cost of low rendering speed and large size of resulting surface. In this paper, we present an approach for visualizing vascular structures by exploiting the local curvature(More)
BACKGROUND The effective geometric modeling of vascular structures is crucial for diagnosis, therapy planning and medical education. These applications require good balance with respect to surface smoothness, surface accuracy, triangle quality and surface size. METHODS Our method first extracts the vascular boundary voxels from the segmentation result,(More)
Cerebral glioma is one of the most aggressive space-occupying diseases, which will exhibit midline shift (MLS) due to mass effect. MLS has been used as an important feature for evaluating the pathological severity and patients’ survival possibility. Automatic quantification of MLS is challenging due to deformation, complex shape and complex grayscale(More)
In this paper, an automatic multi-organ segmentation based on multi-boost learning and statistical shape model search was proposed. First, simple but robust Multi-Boost Clas-sifier was trained to hierarchically locate and pre-segment multiple organs. To ensure the generalization ability of the classifier relative location information between organs, organ(More)