Jinke Wang

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PURPOSE Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. METHODS First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image(More)
To evaluate the diagnostic value of three-dimensional echocardiography (3-DE) in congenital heart disease such as atrial septal defect (ASD) by virtual reality (VR), ten ASDs with different size and shape were created in ten fresh explained porcine hearts. HP SONOS 5500 imaging system was employed for 3-DE reconstructed and visualized by virtual reality(More)
We describe a novel appearance model with optimal combined features to produce the accurate vessel segmentation. It starts with investigating a set of multi-scale vessel features, followed by a weighed approach to optimally combine different features. Then the optimally combined features advantage the appearance model to reveal more detailed information of(More)
This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology(More)
This paper presents a new pulmonary nodules computer-aided detection system in chest CT images utilizing the adaptive fuzzy C-Means (AFCM) technologies. Since rough segmentation of nodules tends to result in high false positive (FP), the main purpose of this study is to reduce the false-positive of candidate nodules via the clustering and classifying(More)
In this paper, an automatic pulmonary nodule segmentation scheme is proposed using modified variable N-quoit filter (VNQ), combined with lung boundary smoothing and correction. The whole scheme is mainly divided into three stages: lung parenchyma segmentation, lung boundary smoothing and correction, and candidate nodules segmentation. In the lung parenchyma(More)