Aims To assess the prognostic value of coronary computed tomography angiography (CTA) and serum biomarkers for the prediction of major adverse cardiac events (MACE) at three-month and one-year follow-ups. Methods and Results A total of 720 patients with acute chest pain and normal electrocardiography (ECG) were included in the prospective cohort study. These patients received both coronary CTA screening and serum biomarkers testing, followed by three-month and one-year follow-ups for the occurrence of major adverse cardiac events (MACE). The primary outcome was the occurrence of MACE, which is defined as acute coronary syndrome (ACS), nonfatal MI, and all-cause mortality. The MACE rate was 17.8% (128 cases) and 25.2% (182 cases) at three-months and one-year follow-up. ApoB/apoA1(OR = 7.45, P < 0.001) and the number of atherosclerotic vessels (OR = 2.86, P < 0.001) were independent predictors for MACE at the three-month follow-up, so were apoB/apoA1 (OR = 5.23, P = 0.003), Serum amyloid protein A (SAA, OR = 1.04, P < 0.001) and the number of atherosclerotic vessels (OR = 2.54, P < 0.001) at the one-year follow-up. While apoB/apoA1 suggested its sensitivities of 84% for predicting MACE at three-month follow-ups, the number of atherosclerotic vessels had 81% specificity at one-year follow-up. Conclusions Among patients with acute chest pain and normal ECG, apoB/apoA1, SAA and the number of atherosclerotic vessels are the most powerful predictors of MACE at three-month and one-year follow-ups.

Geodesic active contour methods are powerful numerical techniques for image segmentation and analysis. However the edge detector in the model ensures that the data on both sides of the contour is as dissimilar as possible, it cannot deal with the requirement that also the interior of a region should be as homogeneous as possible. In this paper, we present a new region based geodesic active contour method. The new method gives a global view of the boundary information within the image. The method here proposed is particularly well adapted to situations where edges are weak and overlap, and the curve initialization is a very bad guess. A number of experiments on CT medical images were performed to evaluate the new method. The experimental results demonstrate the reliability and efficiency of this new method.

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Image processing by Granulometry method is used to measuring microbubbles size distribution in this paper. In this method the size distribution of microbubbles in an image determines without explicitly detecting or segmenting each bubble first. Microbubbles with average sizes about 20 to 5300 micron and air volume fraction from 3 to 42 percent, which have been generated by a microbubble generator device, were measured. Estimated maximum and minimum sizes of microbubbles were compared to direct measuring of images. Excellent comparable results suggested using this method to determination of size distribution, for an extensive domain of void fractions especially in high void fractions while other image processing methods were confined to processing images of fully disperse phases.

A technique based on infrared images acquisition from infrared thermal imaging technology combined visible-light images for image segmentation is introduced. Under the same hardware (infrared camera) acquisition devices captured the same point of the visible-light images and infrared images. The infrared image is used LOG operator to be objective edge. Then gets the original boundary to the larger lattice re-sampling, purpose to eliminate affected with infrared images by noise and ignores the breakpoints edge, the boundary chain code can be shorter. The boundary pixel record with 8 directions chain code .Use the chain code with the visible-light image from the same point of view for image segmentation. Thus, it can get the region of interest (ROI). Use to characteristics of infrared images low-resolution and less information, but sharp change on the edge. Ignore deal on the internal details of the object-region and focus to object-boundary. Use to characteristics of visible-light images color and texture information. Experimental results demonstrate that the Infrared images and visible-light images combined for image segmentation is effective and efficient.