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BACKGROUND In recent years, neuroimaging has been increasingly used as an objective method for the diagnosis of Parkinson's disease (PD). Most previous studies were based on invasive imaging modalities or on a single modality which was not an ideal diagnostic tool. In this study, we developed a non-invasive technology intended for use in the diagnosis of(More)
Some second order PDE-based image restoration models such as total variation (TV) minimization or ROF model of Rudin et al. (Physica D 60, 259–268, 1992) can easily give rise to staircase effect, which may produce undesirable blocky image. LOT model proposed by Laysker, Osher and Tai (IEEE Trans. Image Process. 13(10), 1345–1357, 2004) has alleviated the(More)
Our results of liver sirD were produced by the method proposed in [1] This paper proposes a novel convex variational model for 3-D liver segmentation in CT scan volumes, which presents a variety of challenges including ambiguous edges, tissue adhesion and intensity overlapping between organs/ tissues. To stably delineate weak liver boundaries and fine(More)
PURPOSE Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further(More)
PURPOSE Liver segmentation from computed tomography images is a challenging task owing to pixel intensity overlapping, ambiguous edges, and complex backgrounds. The authors address this problem with a novel active surface scheme, which minimizes an energy functional combining both edge- and region-based information. METHODS In this semiautomatic method,(More)
Interface evolution problems are often solved elegantly by the level set method, which generally requires the time-consuming reinitialization process. In order to avoid reinitialization, we reformulate the variational model as a constrained optimization problem. Then we present an augmented Lagrangian method and a projection Lagrangian method to solve the(More)
PURPOSE It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to background in ultrasound images. In this study, we employ cascade deep convolutional neural networks (CNNs) to(More)
PURPOSE To evaluate the accuracy of shear wave elastography (SWE) in the quantitative diagnosis of liver fibrosis severity. METHODS The published literatures were systematically retrieved from PubMed, Embase, Web of science and Scopus up to May 13th, 2016. Included studies reported the pooled sensitivity, specificity, positive and negative predictive(More)
Image segmentation plays an important role in medical image analysis. The most widely used image segmentation algorithms, region-based methods that typically rely on the homogeneity of image intensities in the regions of interest, often fail to provide accurate segmentation results due to the existence of bias field, heavy noise and rich structures. In this(More)
Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous(More)