BACKGROUND Conventional plain X-ray images of rats,the most common animals used as degeneration models,exhibit unclear vertebral structure and blurry intervertebral disc spaces due to their small size,slender vertebra...BACKGROUND Conventional plain X-ray images of rats,the most common animals used as degeneration models,exhibit unclear vertebral structure and blurry intervertebral disc spaces due to their small size,slender vertebral bodies.AIM To apply molybdenum target X-ray photography in the evaluation of caudal intervertebral disc(IVD)degeneration in rat models.METHODS Two types of rat caudal IVD degeneration models(needle-punctured model and endplate-destructed model)were established,and their effectiveness was verified using nuclear magnetic resonance imaging.Molybdenum target inspection and routine plain X-ray were then performed on these models.Additionally,four observers were assigned to measure the intervertebral height of degenerated segments on molybdenum target plain X-ray images and routine plain X-ray images,respectively.The degeneration was evaluated and statistical analysis was subsequently conducted.RESULTS Nine rats in the needle-punctured model and 10 rats in the endplate-destructed model were effective.Compared with routine plain X-ray images,molybdenum target plain X-ray images showed higher clarity,stronger contrast,as well as clearer and more accurate structural development.The McNemar test confirmed that the difference was statistically significant(P=0.031).In the two models,the reliability of the intervertebral height measured by the four observers on routine plain X-ray images was poor(ICC<0.4),while the data obtained from the molybdenum target plain X-ray images were more reliable.CONCLUSIONMolybdenum target inspection can obtain clearer images and display fine calcification in the imaging evaluation of caudal IVD degeneration in rats,thus ensuring a more accurate evaluation of degeneration.展开更多
Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two comp...Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full force.While the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input data.With a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and decision.It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers.To address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical representations.We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial orientation.Moreover,a text mining technique was applied to extract practical insights from common visualization pitfalls.Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial orientation.The findings showed that the pie chart is the most misused graphical representation,and size is the most critical issue.It was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.展开更多
Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system （AFIS）. Accurate segmentation of a fingerprint will greatly reduce the computation time of ...Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system （AFIS）. Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmenta- tion algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate （Err） and McNemar＇s test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.展开更多
基金Supported by the National Key Research and Development Program of China,No.2017YFA0105404。
文摘BACKGROUND Conventional plain X-ray images of rats,the most common animals used as degeneration models,exhibit unclear vertebral structure and blurry intervertebral disc spaces due to their small size,slender vertebral bodies.AIM To apply molybdenum target X-ray photography in the evaluation of caudal intervertebral disc(IVD)degeneration in rat models.METHODS Two types of rat caudal IVD degeneration models(needle-punctured model and endplate-destructed model)were established,and their effectiveness was verified using nuclear magnetic resonance imaging.Molybdenum target inspection and routine plain X-ray were then performed on these models.Additionally,four observers were assigned to measure the intervertebral height of degenerated segments on molybdenum target plain X-ray images and routine plain X-ray images,respectively.The degeneration was evaluated and statistical analysis was subsequently conducted.RESULTS Nine rats in the needle-punctured model and 10 rats in the endplate-destructed model were effective.Compared with routine plain X-ray images,molybdenum target plain X-ray images showed higher clarity,stronger contrast,as well as clearer and more accurate structural development.The McNemar test confirmed that the difference was statistically significant(P=0.031).In the two models,the reliability of the intervertebral height measured by the four observers on routine plain X-ray images was poor(ICC<0.4),while the data obtained from the molybdenum target plain X-ray images were more reliable.CONCLUSIONMolybdenum target inspection can obtain clearer images and display fine calcification in the imaging evaluation of caudal IVD degeneration in rats,thus ensuring a more accurate evaluation of degeneration.
文摘Data visualization blends art and science to convey stories from data via graphical representations.Considering different problems,applications,requirements,and design goals,it is challenging to combine these two components at their full force.While the art component involves creating visually appealing and easily interpreted graphics for users,the science component requires accurate representations of a large amount of input data.With a lack of the science component,visualization cannot serve its role of creating correct representations of the actual data,thus leading to wrong perception,interpretation,and decision.It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers.To address common pitfalls in graphical representations,this paper focuses on identifying and understanding the root causes of misinformation in graphical representations.We reviewed the misleading data visualization examples in the scientific publications collected from indexing databases and then projected them onto the fundamental units of visual communication such as color,shape,size,and spatial orientation.Moreover,a text mining technique was applied to extract practical insights from common visualization pitfalls.Cochran’s Q test and McNemar’s test were conducted to examine if there is any difference in the proportions of common errors among color,shape,size,and spatial orientation.The findings showed that the pie chart is the most misused graphical representation,and size is the most critical issue.It was also observed that there were statistically significant differences in the proportion of errors among color,shape,size,and spatial orientation.
基金Acknowledgements This paper was supported by the National High Technology Research and Development Program of China （2008AA01Z411）, the National Natural Science Foundation of China （Grant Nos. 60902083, 60803151, and 60875018）, the Beijing Natural Science Fund （4091004）, and the Fundamental Research Funds for the Central Universities （K50510100003）.
文摘Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system （AFIS）. Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmenta- tion algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate （Err） and McNemar＇s test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.
