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Abstract: Obtaining the accurate disparity of each pixel quickly is the goal of stereo matching, but it is very difficult for the 3D labels-based methods due to huge search space of 3D labels, especially for highresolution images. We present an novel two-stage optimization strategy to get the accurate disparity map for high-resolution stereo image efficiently, which includes feature points optimization and superpixel optimization. In the first stage, we construct the support points including edge points and robust points for triangulation, which is used to extract feature points and then perform spatial propagation and random refinement to get the candidate 3D label sets. In the stage of superpixel optimization, we update per pixel labels of the corresponding superpixels using the candidate label sets, and then perform spatial propagation and random refinement. In order to provide more prior information to identify weak texture and textureless areas, we design the weight combination of "intensity + gradient + binary image" for constructing an optimal minimum spanning tree (MST) to compute the aggregated matching cost, and obtain the labels of minimum aggregated matching cost. We also design local patch surrounding the corresponding superpixel to accelerate our algorithm in parallel. The experimental result shows that our method achieves a good trade-off between running time and accuracy, including KITTI and Middlebury benchmark, which are the standard benchmarks for testing the stereo matching methods.
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A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape, position and other information of lesions from magnetic resonance image. First, fuzzy c-means model is applied to extract the initial lesion region. Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. The final lesion segmentation is achieved by evolving the level set contour. The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images. The proposed method helps to reduce the work of radiologists significantly, which is useful in clinical application.
Subject(s)
Humans , Algorithms , Magnetic Resonance Imaging , Multiple Sclerosis , Diagnostic ImagingABSTRACT
Objective To improve the image quality of the electrical impedance tomography (EIT) by introducing the prior information into the regularization matrix.Methods The linear combination of the conductivity was established by background conductivity of dynamic variation,the covariance matrix was used here to remove the correlation between the background conductivity,and this prior information was introduced to construct the regularization matrix.Resnlts Compared with the traditional regularization matrix,the one involving in the prior information on the dynamic background gained more stable and better images.Conclusion Trials prove the efficacy of the regularization matrix on EIT imaging in 1 respiratory cycles (or heart beat),and following related researches may find theoretical references and support for feasibility.
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PURPOSE: The purpose of this study was to examine the effects of prior information about ICU environment on the anxiety and environmental stress of cardiac surgery ICU patients. METHODS: A non-equivalent control group non-synchronized quasi-experimental research design was used. Participants were 60 (control 30, experimental 30) patients who had been admitted to ICU. Prior information about the ICU environment was provided to the experimental group. The anxiety level of subjects was measured by State-Trait Anxiety Inventory (STAI) and the stress level of subjects was measured by the Intensive Care Unit Environmental Stressor Scale (ICUESS). Data were analyzed using a Chi-square test or a Fisher's exact test, independent samples t-test, and paired samples t-test. RESULTS: There was no difference in Anxiety (t=-0.58, p=.563), but there was a significant difference in environmental stress (t=10.46, p<.001). CONCLUSION: Providing prior information would be an effective nursing intervention to reduce environmental stress.
Subject(s)
Humans , Anxiety , Intensive Care Units , Nursing , Research Design , Thoracic SurgeryABSTRACT
The reverse problem of Electrical impedance tomography(EIT) is a highly ill-posed problem.It is concluded that spatial prior information could improve the final image quality.This paper proposes a new method for obtaining prior information.By this method,the inspected cross-section contour and internal structure for EIT can be achieved.