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A computation method of dual-energy X-ray imaging

A computation method of dual-energy X-ray imaging

引用

Medical Imaging 2006: Physics of Medical Imaging

作者： Mou, Xuanqin Tang, Shaojie Hong, Wei Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi 710049 China

ISBN: (纸本)0819461857

Dual-energy X-ray imaging is an important method of medical imaging, capable of not only obtaining spatial information of imaging object but also disclosing its chemical components, and has many applications in clinic. The current computation methods of dual-energy imaging are still based on the model of mono-energy spectrum imaging with some linear calibration, while they are incapable to reflect correctly the physical characteristics of dual-energy imaging and obstruct deeper research in this field. The article presents a new medical X-ray imaging model in accordance with physics of imaging and its corresponding computational method. The computation process includes two steps: first, to compute two attenuation parameters that have clear physical meaning: equivalent electron density and attenuation parameter of photoemission;then to compute the components of high- and low-density mass through a group of simple equation with two variables. Experiments showed that such method has quite a satisfactory precision in theory, that is, the solutions of parameters under different exposure voltages and thickness of tissue for several main tissues of human body are much low in deviations, whose quotient of standard deviation divided by mean are mostly under 0.1%, and at most 0.32%. The method provides not only a new computational way for dual-energy X-ray imaging, but also a feasible analysis for its nature. In addition, the method can be used to linearly rectify data of dual-energy CT and analyze the chemical component of reconstructed object by means of parameters clear in physics.

关键词： Medical imaging

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Using steepest descent method to refine the node positions in wireless sensor networks

Using steepest descent method to refine the node positions i...

引用

IET Conference on Wireless, Mobile and Sensor Networks 2007, CCWMSN'07

作者： Shi, Q.Q. Huo, H. Fang, T. Li, R.D. Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghi 200240 China

ISBN: (纸本)9780863418365

Refinement is a necessary and effective step in some node localization schemes of wireless sensor networks (WSN). Suitable refinement procedure can improve the node localization accuracy and raise the robustness of the localization algorithm. However, most existing refinement algorithms are costly duo to complex computation and frequent communication, and may induce serious coverage problem duo to nonconvergent iterations. In view of above facts, Steepest descent method is proposed to be used as the refinement algorithm in this paper, and corresponding simulation experiments are done to testify its feasibility and validity. The results show that steepest descent method can optimize the node positions to a fairish accuracy extent, and compared with existing refinement methods, it outperforms in communication cost, computation cost, and coverage rate.

关键词： Steepest descent method

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Implementation of penalized-likelihood statistical reconstruction for polychromatic dual-energy CT

Implementation of penalized-likelihood statistical reconstru...

引用

Medical Imaging 2009: Physics of Medical Imaging

作者： Xu, Qiong Mou, Xuanqin Tang, Shaojie Wei, Hong Yizhai, Zhang Luo, Tao Institute of Image processing and Pattern recognition Xi'an Jiaotong University Xi'an Shaanxi 710049 China

ISBN: (纸本)9780819475091

This paper presents a statistical reconstruction algorithm for dual-energy (DE) CT of polychromatic x-ray source. Each pixel in the imaged object is assumed to be composed of two basis materials (i.e., bone and soft tissue) and a penalizedlikelihood objective function is developed to determine the densities of the two basis materials. Two penalty terms are used respectively to penalize the bone density difference and the soft tissue density difference in neighboring pixels. A gradient ascent algorithm for monochromatic objective function is modified to maximize the polychromatic penalizedlikelihood objective function using the convexity technique. In order to reduce computation consumption, the denominator of the update step is pre-calculated with reasonable approximation replacements. Ordered-subsets method is applied to speed up the iteration. Computer simulation is implemented to evaluate the penalized-likelihood algorithm. The results indicate that this statistical method yields the best quality.©2009 SPIE.

关键词： Pixels

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Multi-scale nonlinear contrast enhancement of radiographics based on human contrast sensitivity

Multi-scale nonlinear contrast enhancement of radiographics ...

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5th International Conference on Visual Information Engineering, VIE 2008

作者： Zhang, Min Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi 710049 China

ISBN: (纸本)9780863419140

Medical imaging techniques like computed/digital radiography (CR/DR) have introduced a formidably powerful tool in medicine. image enhancement takes an important roll in the CR/DR computerized analysis process. Much effort has been put into the area of image enhancement. However, conventional multi-scale methods have the drawback of the introduction of severe visible artifacts while large structures are enhanced strongly. This paper presents a nonlinear multi-scale medical image contrast enhancement method for the improvement of medical image quality. More specifically, a novel nonlinear enhancement function is proposed incorporated with human visual local perceptual contrast. The proposed work provides the advantages of enhancing or preserving image contrast while suppressing visible artifacts. To quantitatively compare the performance of the proposed method, the average local variances are used as comparison criteria. Results demonstrate the superiority of the proposed method. Our results show that the proposed method has the potential to become useful for improvement of image quality of medical images. ©2008 The Institution of Engineering and technology.

关键词： image enhancement

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A novel approach to forecast weakly regular traffic status

A novel approach to forecast weakly regular traffic status

引用

11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008

作者： Zhang, Yang Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424421121

How to accurately predict traffic data with weak regularity is difficult for various forecasting models. In this paper, least squares support vector machines (LS-SVMs) are proposed to deal with such a problem. It is the first time to apply the technique and analyze the forecast performance in the field. For comparison purpose, other three baseline predictors are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. © 2008 IEEE.

关键词： Forecasting

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Feature selection for multi-class problems using support vector machines

Feature selection for multi-class problems using support vec...

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8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence

作者： Li, Guo-Zheng Yang, Jie Liu, Guo-Ping Xue, Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200030 China

ISBN: (纸本)3540228179

Since feature selection can remove the irrelevant features and improve the performance of learning systems, it is an crucial step in machine learning. The feature selection methods using support vector machines have obtained satisfactory results, but the previous works are usually for binary classification, and needs auxiliary techniques to be extended to multiple classification. In this paper, we propose a prediction risk based feature selection method using multiple classification support vector machines. The performance of the proposed method is compared with the previous methods of optimal brain damage based feature selection methods using binary support vector machines. The results of experiments on UCI data sets show that prediction risk based feature selection method obtains better results than the previous methods using support vector machines for multiple classification problems. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Classification (of information)

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An improvement on discrete wavelet transform-based algorithm for vehicle classification in wireless sensor networks

An improvement on discrete wavelet transform-based algorithm...

引用

2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006

作者： Xiao, Shen Sheng, Cong Wan Hong, Huo Tao, Fang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)078039514X

In this paper, we present an improvement on the discrete wavelet transform (DWT)-based feature extraction algorithm used in vehicle classification in Wireless Sensor Networks (WSN) [1] by providing a rule of selecting the best number of resolution levels which will improve the classification rate and reduce the energy consumption in local computation. DWT can provide time-frequency multi-resolution analysis (MRA) which is fast and can greatly reduce the dimensions of feature vectors. We found out that, when using wavelet, the number of resolution levels will greatly influence the effect of feature extraction and energy consumption. In this paper, Different numbers of resolution levels and corresponding time of computation are discussed. And a rule of selecting the best number of resolution levels is given. To test our algorithm, acoustic signals emitted by two kinds of vehicles are investigated, and a k-Nearest-Neighbor method is used as classifier. The experiment shows that the rule provided can obviously improve the classification rate. © 2006 IEEE.

关键词： Wireless sensor networks

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Head pose estimation from low-resolution image with hough forest

Head pose estimation from low-resolution image with hough fo...

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Chinese Conference on pattern recognition

作者： Zhang, Ming Li, Ke Liu, Yuncai Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781424472109

An approach for head pose estimation has been proposed in this paper using Hough forest. The estimation of pose are generated by voting from image patches as in a Hough transform. The basic idea is that image patches which contain eyes, hair or neck can give rich information about the head position and orientation. The voting process is implemented by randomized forest which is an efficient and robust tool for classification and regression. The method is quantitatively evaluated by comparing the estimated pose to the ground truth. ©2010 IEEE.

关键词： Hough transforms

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Matching based highlight removal

Matching based highlight removal

引用

International Conference on Multimedia technology

作者： Xu, Yiren Wang, Fujian Zhao, Yuming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424478743

This paper presents a three-step framework to remove the highlight exists on objects in certain conditions. Unlike traditional HDR (High Dynamic Range) technology requires multiple registrated image sequence;our method needs only two arbitrary images. SURF (Speeded Up Robust Features) matching algorithm is first applied to find corresponding point pairs between images;homography is then found by perspective transformation theory;minimum gray selection is used at last to eliminated the highlight and fuse related regions. Experimental results are given to demonstrate the performance of our method. ©2010 IEEE.

关键词： Electronics industry

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Dictionary learning based statistical interior reconstruction without a prior knowledge 10

Dictionary learning based statistical interior reconstructio...

引用

Developments in X-Ray Tomography X

作者： Shi, Yongyi Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an Shaanxi710049 China

ISBN: (纸本)9781510603257

Despite the significantly practical utilities of interior tomography, it still suffers from severe degradation of direct current (DC) shift artifact. Existing literature suggest to introducing prior information of object support (OS) constraint or the zeroth order image moment, i.e., the DC value into interior reconstruction to suppress the shift artifact, while the prior information is not always available in practice. Aimed at alleviating the artifacts without prior knowledge, in this paper, we reported an approach on the estimation of the object support which could be employed to estimate the zeroth order image moment, and hence facilitate the DC shift artifacts removal in interior reconstruction. Firstly, by assuming most of the reconstructed object consists of soft tissues that are equivalent to water, we reconstructed a virtual OS that is symmetrical about the interior region of interest (ROI) for the DC estimation. Hence the DC value can be estimated from the virtual reconstruction. Secondly, a statistical iterative reconstruction incorporated with the sparse representation in terms of learned dictionary and the constraint in terms of image DC value was adopted to solve the interior tomography. Experimental results demonstrate that the relative errors of the estimated zeroth order image moment are 4.7% and 7.6%, corresponding to the simulated data of a human thorax and the real data of a sheep lung, respectively. Reconstructed images with the constraint of the estimated DC value exhibit greatly superior image quality to that without DC value constraint. © Copyright 2016 SPIE.

关键词： image segmentation

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