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Medical X-Ray Image Enhancement Based on Kramer's PDE Model

Journal of Electronic Science and Technology of China 2007年第2期5卷 187-190页

作者： Yan-Fei Zhao Qing-Wei Gao De-Xiang Zhang Yi-Xiang Lu Key Lab. of Intelligent Computing and Signal Processing of MOE Anhui University. Hefei 230039 China School of Electronic Science and Technology Anhui University Hefei 230039 China

The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation （PDE） model, Kramer＇s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer＇s PDE model. The study suggests that the Kramer＇s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.

关键词： Terms-Enhancement nonlinear partial differential equation （PDE） partial differential equation model X-ray image.

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Point matching of two images under large different resolution

Point matching of two images under large different resolutio...

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MIPPR 2007: Automatic Target Recognition and Image Analysis;and Multispectral Image Acquisition

作者： Ruan, Lei Hu, Maolin Lab. of Intelligent Computing and Signal Processing AnHui University Hefei AnHui 230039 China Key Laboratory of Wireless Sensor Network and Communication Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

ISBN: (纸本)9780819469502

Image matching is the first step in almost any 3D computer vision task, and hence has received extensive attention. In this paper, the problem is addressed from a novel perspective, which is different from the classic stereo matching paradigm. Two images with different resolutions, that is high resolution versus low resolution are matched. Since the high resolution image only corresponds to a small region of the low resolution one, the matching task therefore consists in finding a small region in the low resolution image that can be assigned to the whole high resolution image under the plane similarity transformation, which can be defined by the local area correlation coefficient to match the interest points and rectified by similarity transform. Experiment shows that our matching algorithm can be used for scale changing up to a factor of 6. And it is successful to deal with the point matching between two images under large scale.

关键词： Image matching

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Medical X-Ray Image Enhancement Based on Kramer's PDE Model

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Journal of Electronic Science and Technology 2007年第2期000卷 187-190页

作者： Y.-F.Zhao Q.-W.Gao D.-X.Zhang Y.-X.Lu Key Lab. of Intelligent Computing and Signal Processing of MOE Anhui University. Hefei 230039 China School of Electronic Science and Technology Anhui University Hefei 230039 China

The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation (PDE) model, Kramer’s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer’s PDE model. The study suggests that the Kramer’s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.

关键词： Enhancement nonlinearpartialdifferentialequation(PDE) partialdifferentialequationmodel X-rayimage

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Good Lattice Swarm Algorithm for Constrained Engineering Design Optimization

Good Lattice Swarm Algorithm for Constrained Engineering Des...

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第三届IEEE无线通讯、网络技术暨移动计算国际会议

作者： Shoubao Su Jiwen Wang Wangkang Fan Xibing Yin Key Lab of Intelligent Computing & Signal Processing of the National Education Ministry Anhui Unive Key Lab of Intelligent Computing & Signal Processing of the National Education Ministry Anhui Unive Gear Institute of Lu'an Automotive Gear Manufacture Co. Ltd Anhui Jianghuai Automotive Group Lu'an

Engineering optimization in the intelligence swarm remains to be a challenge. Recently, a novel optimization method based on number-theory and particle swarm, good lattice swarm optimization algorithm(GLSO), is introduced, which intends to produce faster and better global search ability and more accurate convergence because it has a solid theoretical basis. In this paper, four models of constructing good point set are introduced and the GLSO based on new models is rewritten. Some applications of the new model on constrained engineering via employing a penalty function approach suggest that the presented algorithm is potentially a powerful search technique for solving complex engineering design optimization problems.

关键词： good lattice points particle swarm optimization constrained engineering optimization penalty function.

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Image Reconstruction Based on Sub-Gaussian Random Projection

Image Reconstruction Based on Sub-Gaussian Random Projection

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International Conference on Image and Graphics (ICIG)

作者： Hong Fong Quanbing Zhang Sui Wei College of Science Hefei University of Technology Hefei China Key Lab of Intelligent Computing and Signal Processing Anhui University Hefei China

In this paper, we introduce the sub-Gaussian random projection into compressed sensing (CS) theory and present two new kinds of CS measurement matrices: sparse projection matrix and very sparse projection matrix. By the tail bounds for sub-Gaussian random projection, we present the proof of how these new matrices satisfying the necessary condition for CS measurement matrix. Further, we expatiate that owe to their sparsity, new matrices greatly simplify the projection operation during images reconstruction, which greatly improves the speed of reconstruction. The results of simulated and real experiments show that with a certain number of measurements, new matrices both achieve good measurement effect and can acquire exact reconstruction by them. Last, the comparison of reconstruction results respectively adopting new matrices and Gaussian measurement matrix is conducted.

关键词： Image reconstruction Sparse matrices Compressed sensing Tail Couplings Graphics signal processing Educational institutions Linear programming Matrix decomposition

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A novel image content authentication algorithm based on Laplace spectra feature

A novel image content authentication algorithm based on Lapl...

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26th Chinese Control Conference, CCC 2007

作者： Wanli, Lv Yutang, Guo LuoBin Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230039 China Computer Department Anhui Institute of Education Hefei 230061 China

ISBN: (纸本)7900719229

A novel image content authentication algorithm based on Laplace spectra was proposed. Outstanding feature points are extracted from the original image and a cipher point is inserted. A relational graph is then built, and the Laplace spectra of the graph are calculated to serve as image features. The Laplace spectra are quantized then embedded into the original image as a watermark. In the authentication step, the Laplace spectra of the authenticating image are calculated and compared with that of the watermark embedded in the authenticating image. If both of the spectra are identical, the image passes the authentication test. Otherwise, the tamper is found. The experimental results show that the proposed authentication algorithm can effectively detect the event and the location when the original image content is tampered viciously.

关键词： Laplace equation

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Protein structure class analysis based on multifractal and quotient space

Protein structure class analysis based on multifractal and q...

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26th Chinese Control Conference, CCC 2007

作者： Tingting, Zheng Junjun, Mao Tao, Wu Jie, Song Key Lab. of Intelligent Computing and Signal Processing Anhui University Hefei 230039 Anhui China School of Mathematics and Computational Science Anhui University Hefei 230039 Anhui China

ISBN: (纸本)7900719229

The multifractal spectrum of protein feature sequences was computed and analyzed with the multifractal. The parameters of multifractal spectra were used to describe hierarchically refined structure of protein feature sequences and pop out the singularity of local sequences. And with using quotient space granularity computing theory power gene a of multifractal was chosen wilder. Constructing 2D space, and it presented good efficiency in structure classing, which is favor of predicting protein structure class.

关键词： Proteins

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Study on moving target detection to passive radar based on FM broadcast transmitter

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Chinese Journal of Systems Engineering and Electronics 2007年第3期000卷

作者： Zhu Jiabing Tao Liang Hong Yi Education Ministry Key Lab of Intelligent Computing and Signal Processing Anhui Univ.Hefei 230039P.R.China Inst.of Signal Processing East China Research Inst.of Electronic EngineeringHefei 230031P.R.China Education Ministry Key Lab of Intelligent Computing and Signal Processing Anhui Univ.Hefei 230039P.R.China

Target detection by a noncooperative illuminator is a topic of general interest in the electronic warfare *** of all,direct-path interference(DPI)suppression which is the technique of bottleneck of moving target detection by a noncooperative frequency modulation(FM) broadcast transmitter is analyzed in this article;Secondly,a space-time-frequency domain synthetic solution to this problem is introduced:Adaptive nulling array processing is considered in the space domain,DPI cancellation based on adaptive fractional delay interpolation(AFDI)technique is used in planned time domain,and long-time coherent integration is utilized in the frequency domain;Finally,an experimental system is planned by considering FM broadcast transmitter as a noncooperative illuminator,Simulation results by real collected data show that the proposed method has a better performance of moving target detection.

关键词： passive radar direct path interference adaptive beamforming adaptive fractional delay interpolation long-time coherent integration.

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Solving multi-object radar cross section based on wide-angle parabolic equation method

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Journal of Systems Engineering and Electronics 2006年第4期17卷 722-724页

作者： Huang Zhixiang Wu Qiong Wu Xianliang The Key Lab of Ministry of Education of Intelligent Computing & Signal Processing Anhui Univ. Hefei 230039 P. R. China

Based on a Pade approximation, a wide-angle parabolic equation method is introduced for computing the multiobject radar cross section （RCS） for the first time. The method is a paraxial version of the scalar wave equation, which solves the field by marching them along the paraxial direction. Numerical results show that a single wide-angle parabofic equation run can compute multi-object RCS efficiently for angles up to 45 ° . The method provides anew and efficient numerical method for computation electromagnetics.

关键词： parabolic equation method Pade approximation radar cross section paraxial direction.

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Semi-supervised clustering of corner-oriented attributed graphs

Semi-supervised clustering of corner-oriented attributed gra...

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6th International Conference on Hybrid intelligent Systems and 4th Conference on Neuro-computing and Evolving Intelligence, HIS-NCEI 2006

作者： Tang, Jin Zhang, Chunyan Luo, Bin Key Lab. of Intelligent Computing and Signal Processing Anhui University 230039 China

ISBN: (纸本)0769526624

This paper describes a new algorithm for image semi-supervised clustering. In particular, the proposed approach introduces corner-oriented attributed graphs(COAG) constructed based on modified Harris corner extraction method to represent structure objects . 2D-Laplacianface is used to reduce the dimension of feature matrix obtained from COAG. Feature vector is built just from the output of dimensionality reduction. This vector denotes the input to the classifier. Semisupervised k-mean clustering method (S2KMCM) is carried out as semi-clustering method. Experimental results show that COAG can preserve the structure information of image and S2KFCM can be applied to both clustering and classification tasks by labeled and unlabeled data together. © 2006 IEEE.

关键词： Graph theory

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