Dongqing Chen

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ÐVirtual endoscopy is a computerized, noninvasive procedure for detecting anomalies inside human organs. Several preliminary studies have demonstrated the benefits and effectiveness of this modality. Unfortunately, previous work cannot guarantee that an existing anomaly will be detected, especially for complex organs with multiple branches. In this paper,(More)
Abstract: Segmentation of magnetic resonance (MR) images plays an important role in quantitative analysis of brain tissue morphology and pathology. However, the inherent effect of image-intensity inhomogeneity renders a challenging problem and must be considered in any segmentation method. For example, the adaptive fuzzy c-mean (AFCM) image segmentation(More)
Although cloud computing has the advantages of cost-saving, efficiency and scalability, it also brings about many security issues. Because almost all software, hardware, and application data are deployed and stored in the cloud platforms, there is often the distrust between users and cloud suppliers. To resolve the problem, this paper proposes a risk(More)
In this paper, we introduce an adaptive level set method for segmenting a colon filled with air and opacified fluid in CT colonography. We first use simple thresholding method to remove most of the opacified liquid. Then, closed contours with manual seed initialization are propagated toward the region boundaries through the iterative evolution of an(More)
Curvature-based geometric features have been proven to be important for colonic polyp detection. In this paper, we present an automatic detection framework and color coding scheme to highlight the detected polyps. The key idea is to place the detected polyps at the same locations in a newly created polygonal dataset with the same topology and geometry(More)
Virtual colonoscopy provides a safe, minimal-invasive approach to detect colonic polyps using medical imaging and computer graphics technologies. Residual stool and fluid are problematic for optimal viewing of the colonic mucosa. Electronic cleansing techniques combining bowel preparation, oral contrast agents, and image segmentation were developed to(More)
We present a new fully automatic algorithm for MR image segmentation. The MR image data is first interpolated for an adequate local feature vector on each voxel. Then, a two-level segmentation scheme is applied. One is a data-oriented low level segmentation, which is based on a modified self-adaptive on-line vector quantization technique. The other is a(More)
This paper introduces an adaptive level set method for 3D segmentation of colon tissue in CT colonography filled with air and opacified fluid. First, most of the opacified liquid is removed by a threshold value. The closed contours are propagated toward the desired 3D region boundaries through the iterative evolution of the adaptive level sets function. The(More)
We present a fully automatic algorithm for brain magnetic resonance (MR) image segmentation. The three-dimensional (3D) volumetric MR dataset is first interpolated for an adequate local intensity vector on each voxel. Then a morphology dilation filter and region growing technique are applied to extract the region of brain volume, strapping away the skull,(More)
A theoretically based transformation, which reorders SPECT sinograms degraded by the Poisson noise according to their signal-to-noise ratio (SNR), has been proposed. The transformation is equivalent to the maximum noise fraction (MNF) approach developed for Gaussian noise treatment. It is a two-stage transformation. The first stage is the Anscombe(More)