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Hybrid Segmentation of Mass in Mammograms Using Template Matching and Dynamic Programming

ACADEMIC RADIOLOGY 2010年第11期17卷 1414-1424页

作者： Song, Enmin Xu, Shengzhou Xu, Xiangyang Zeng, Jianye Lan, Yihua Zhang, Shenyi Hung, Chih-Cheng Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Hubei Peoples R China Educ Minist Image Proc & Intelligent Control Key Lab Wuhan Peoples R China Nanning Second Peoples Hosp Nanning Peoples R China Huaihai Inst Technol Sch Comp Engn Lianyungang Peoples R China So Polytech State Univ Sch Comp & Software Engn Marietta GA USA

Rationale and Objectives: Accurate image segmentation for breast lesions is a critical step in computer-aided diagnosis systems. The objective of this study was to develop a robust method for the automatic segmentation of breast masses on mammograms to extract feasible features for computer-aided diagnosis systems. Materials and Methods: The data set used in this study consisted of 483 regions of interest extracted from 328 patients. A hybrid method for segmenting breast masses was proposed on the basis of the template-matching and dynamic programming techniques. First, a template-matching technique was used to locate and obtain the rough region of masses. Then, on the basis of this rough region, a local cost function for dynamic programming was defined. Finally, the optimal contour was derived by applying dynamic programming as an optimization technique. The performance of this proposed segmentation method was evaluated using area-based and boundary distance-based similarity measures based on radiologists' manually marked annotations. A comparison with three different segmentation algorithms on the data set was provided. Results: The mean overlap percentage for our proposed hybrid method was 0.727 +/- 0.127, whereas those for Timp and Karssemeijer's dynamic programming method, Song et al's plane-fitting and dynamic programming method, and the normalized cut segmentation method were 0.657 +/- 0.216, 0.636 +/- 0.190, and 0.562 +/- 0.199, respectively. All P values for the measure distribution of our proposed method and the other three algorithms were <.001. Conclusions: A hybrid method based on the template-matching and dynamic programming techniques was proposed to segment breast masses on mammograms. Evaluation results indicate that the proposed segmentation method can improve the accuracy of mass segmentation compared to three other algorithms. The proposed segmentation method shows better performance and has great potential in improving the accuracy of computer-a

关键词： Computer-aided diagnosis mass segmentation mammogram template matching dynamic programming

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Quaternion-based color image filtering for impulsive noise suppression

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JOURNAL OF ELECTRONIC IMAGING 2010年第4期19卷 043003-043003-12页

作者： Jin, Lianghai Liu, Hong Xu, Xiangyang Song, Enmin Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Peoples R China

It is difficult to precisely detect all impulsive noise in color images due to the nonstationarity caused by edges and fine details. For many pixels, we can not absolutely classify them as noisy or noise-free, but can only describe them using the likelihood that they are corrupted by impulsive noise. Based on this consideration, we present a new filtering solution to removing impulsive noise from color images. The proposed method first utilizes the unit transforms of quaternions to represent the chromaticity difference of two color pixels, and then divides the image into noise-free and possible noisy pixels. Finally it performs adaptive weighted vector median filtering operations on only the possible noisy pixels to suppress noise. The new weighting mechanism is based on a joint spatial/quaternion-chromaticity criterion, which ensures that pixels with different contamination likelihoods have different contributions to the filter's output. The extensive simulation results indicate that the proposed method significantly outperforms some other well-known multichannel filtering techniques. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3491498]

关键词： image filtering Contamination Simulations Transform theory

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Multi-channel Hammerstein identification

Multi-channel Hammerstein identification

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作者： Wei, Yue Zhang, Hai-Tao Chen, Michael ZhiQiang Zhou, Tao State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Department of Automation Nanjing University of Science and Technology Nanjing 210094 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China Department of Physics University of Fribourg Chemin du Muse CH-1700 Fribourg Switzerland

ISBN: (纸本)9787894631046

The Hammerstein systems, consisting of a zero-memory nonlinearity followed by a linear dynamic function, exists universally in industrial, chemical, physical and biological systems. Thus an effective modelling method for Hammerstein systems is critical for both relevant scientific research and engineering applications. We propose a novel Hammerstein identification approach, in which a multi-channel mechanism is used to separate the coefficients of the linear and nonlinear blocks more completely. Compared with traditional single-channel identification algorithms, the present identification method can enhance the approximation accuracy remarkably under the weak condition on the persistent excitation (PE) condition of the inputs.

关键词： Singular value decomposition

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Solving NP-complete problems by spiking neural P systems with budding rules

Solving NP-complete problems by spiking neural P systems wit...

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10th International Workshop on Membrane Computing, WMC 2009

作者： Ishdorj, Tseren-Onolt Leporati, Alberto Pan, Linqiang Wang, Jun Computational Biomodelling Laboratory Åbo Akademi University Department of Information Technologies 20520 Turku Finland Università degli Studi di Milano - Bicocca Dipartimento di Informatica Sistemistica e Comunicazione Viale Sarca 336/14 20126 Milano Italy Key Laboratory of Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Research Group on Natural Computing Department of CS and AI University of Sevilla Avda Reina Mercedes s/n 41012 Sevilla Spain

ISBN: (纸本)3642114660

Inspired by the growth of dendritic trees in biological neurons, we introduce spiking neural P systems with budding rules. By applying these rules in a maximally parallel way, a spiking neural P system can exponentially increase the size of its synapse graph in a polynomial number of computation steps. Such a possibility can be exploited to efficiently solve computationally difficult problems in deterministic polynomial time, as it is shown in this paper for the NP-complete decision problem sat. © 2010 Springer-Verlag.

关键词： Computational complexity

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The method of image restoration in the environments of dust

The method of image restoration in the environments of dust

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IEEE International Conference on Mechatronics and Automation

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education TaiYuan University of Technology TaiYuan China College of Mechanical Engineering TaiYuan University of Technology TaiYuan China Institute for Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology WuHan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, a particle swarm optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with dual threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： image edge detection image restoration Degradation Atmospheric modeling Optical scattering Optical imaging

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Study on the Method of image Restoration in the Environment of Dust

Study on the Method of Image Restoration in the Environment ...

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International Conference on Wireless Communications, Networking and Mobile Computing (WiCom)

作者： Yuanyu Wang Yuanzong Li Tianxu Zhang Department of Computer Fundamental Education Taiyuan University Of Technology Taiyuan China College of Mechanical Engineering Taiyuan University Of Technology Taiyuan China Institute of Pattern Recognition and A.I State Key Laboratory of Image Processing and Intelligent Control HuaZhong University of Science and Technology Wuhan China

In order to solve the problem of image degradation caused by dust environments, an image degradation model considering multiple scattering factors caused by dust was first established using the first-order multiple scattering method. Then, a dark channel prior principle was applied to present an image restoration algorithm based on the image degradation model. Finally, GA optimization algorithm was applied to optimize the atmospheric light and the exposure parameters. This optimization algorithm was established according to the criterion of the image evaluation based on kirsch operator with automatic threshold. By using the method an optimistic result of image restoration was obtained. The experimental results have shown that the method not only enhanced luminance and contrast, but also discovered more detail edges information. The method provided a foundation for target recognition in the dust environments.

关键词： image edge detection image restoration Scattering Degradation Gallium Atmospheric modeling Genetic algorithms

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Single channel speech separation with a frame-based pitch range estimation method in modulation frequency

2010 5th International Symposium on Telecommunications, IST ...

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2010 5th International Symposium on Telecommunications, IST 2010 2010年 609-613页

作者： Mahmoodzadeh, A. Abutalebi, H.R. Soltanian-Zadeh, H. Sheikhzadeh, H. Speech Proc. Research Lab. ECE Dept. Yazd University Iran Control and Intelligent Processing Center of Excellence University of Tehran Iran EE Dept. Amirkabir University of Technology Tehran Iran Idiap Research Institute Martigny Switzerland Image Analysis Lab. Henry Ford Health System Detroit United States

ISBN: (纸本)9781424481835

Computational Auditory Scene Analysis (CASA) has attracted a lot of interest in segregating speech from monaural mixtures. In this paper, we propose a new method for single channel speech separation with frame-based pitch range estimation in modulation frequency domain. This range is estimated in each frame of modulation spectrum of speech by analyzing onsets and offsets. In the proposed method, target speaker is separated from interfering speaker by filtering the mixture signal with a mask extracted from the modulation spectrogram of mixture signal. Systematic evaluation shows an acceptable level of separation comparing with classic methods. © 2010 IEEE.

关键词： Frequency estimation

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Determination of pitch range based on onset and offset analysis in modulation frequency domain

2010 5th International Symposium on Telecommunications, IST ...

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2010 5th International Symposium on Telecommunications, IST 2010 2010年 604-608页

作者： Mahmoodzadeh, A. Abutalebi, H.R. Soltanian-Zadeh, H. Sheikhzadeh, H. Speech Proc. Research Lab. ECE Dept. Yazd University Yazd Iran Control and Intelligent Processing Center of Excellence University of Tehran Tehran Iran EE Dept. Amirkabir University of Technology Tehran Iran Idiap Research Institute Martigny Switzerland Image Analysis Lab. Henry Ford Health System Detroit United States

ISBN: (纸本)9781424481835

Auditory scene in a natural environment contains multiple sources. Auditory scene analysis (ASA) is the process in which the auditory system segregates a scene into streams corresponding to different sources. The determination of range of pitch frequency is necessary for segmentation. We propose a system to determine the range of pitch frequency by analyzing onsets and offsets in modulation frequency domain. In the proposed system, first the modulation spectrum of speech is calculated and then, in each subband onsets and offsets will be detected. Thereafter, the segments are generated by matching corresponding onset and offset front. Finally, by choosing the desired segments, the rage of pitch frequency is determined. Systematic evaluation shows that the range of pitch frequency is estimated with good accuracy. © 2010 IEEE.

关键词： Frequency modulation

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Fuzzy support vector machine for classification of EEG signals using wavelet-based features

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MEDICAL ENGINEERING & PHYSICS 2009年第7期31卷 858-865页

作者： Xu, Qi Zhou, Hui Wang, Yongji Huang, Jian Huazhong Univ Sci & Technol Dept Control Sci & Technol Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Translation of electroencephalographic (EEG) recordings into control signals for brain-computer interface (BCI) systems needs to be based on a robust classification of the various types of information. EEG-based BCI features are often noisy and likely to contain outliers. This contribution describes the application of a fuzzy support vector machine (FSVM) with a radial basis function kernel for classifying motor imagery tasks, while the statistical features over the set of the wavelet coefficients were extracted to characterize the time-frequency distribution of EEG signals. In the proposed FSVM classifier, a low fraction of support vectors was used as a criterion for choosing the kernel parameter and the trade-off parameter, together with the membership parameter based solely on training data. FSVM and support vector machine (SVM) classifiers outperformed the winner of the BCI Competition 2003 and other similar studies on the same Graz dataset, in terms of the competition criterion of the mutual information (MI), while the FSVM classifier yielded a better performance than the SVM approach. FSVM and SVM classifiers perform much better than the winner of the BCI Competition 2005 on the same Graz dataset for the subject 03 according to the competition criterion of the maximal MI steepness, while the FSVM classifier outperforms the SVM method. The proposed FSVM model has potential in reducing the effects of noise or outliers in the online classification of EEG signals in BCIs. (C) 2009 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： Brain-computer interface Discrete wavelet transform Fuzzy support vector machine Motor imagery Electroencephalogram

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Modeling of cortical signals using echo state networks

Modeling of cortical signals using echo state networks

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MIPPR 2009 - Pattern Recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Zhou, Hanying Wang, Yongji Huang, Jiangshuai Key Lab. for Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology China

ISBN: (纸本)9780819478078

Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Brain

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