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Energy enhanced tissue texture in spectral computed tomography for lesion classification

Visual Computing for Industry,Biomedicine,and Art 2019年第1期2卷 138-149页

作者： Yongfeng Gao Yongyi Shi Weiguo Cao Shu Zhang Zhengrong Liang Department of Radiology Stony Brook UniversityStony BrookNY 11794USA Institute of Image Processing and Pattern Recognition Xi’an Jiaotong UniversityXi’an 710049ShanxiChina Departments of Radiology Biomedical EngineeringComputer Scienceand Electrical EngineeringStony Brook UniversityStony BrookNY 11794USA

Tissue texture reflects the spatial distribution of contrasts of image voxel gray levels,i.e.,the tissue heterogeneity,and has been recognized as important biomarkers in various clinical *** computed tomography(CT)is believed to be able to enrich tissue texture by providing different voxel contrast images using different X-ray ***,this paper aims to address two related issues for clinical usage of spectral CT,especially the photon counting CT(PCCT):(1)texture enhancement by spectral CT image reconstruction,and(2)spectral energy enriched tissue texture for improved lesion *** issue(1),we recently proposed a tissue-specific texture prior in addition to low rank prior for the individual energy-channel low-count image reconstruction problems in PCCT under the Bayesian *** results showed the proposed method outperforms existing methods of total variation(TV),low-rank TV and tensor dictionary learning in terms of not only preserving texture features but also suppressing image *** issue(2),this paper will investigate three models to incorporate the enriched texture by PCCT in accordance with three types of inputs:one is the spectral images,another is the cooccurrence matrices(CMs)extracted from the spectral images,and the third one is the Haralick features(HF)extracted from the *** were performed on simulated photon counting data by introducing attenuationenergy response curve to the traditional CT images from energy integration *** results showed the spectral CT enriched texture model can improve the area under the receiver operating characteristic curve(AUC)score by 7.3%,0.42%and 3.0%for the spectral images,CMs and HFs respectively on the five-energy spectral data over the original single energy data *** CM-and HF-inputs can achieve the best AUC of 0.934 and *** texture themed study shows the insight that incorporating clinical important prior information,e.g.,tiss

关键词： Tissue texture Spectral computed tomography Lesion classification Machine learning Bayesian reconstruction

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Extraction of complex wavelet features for iris recognition

Extraction of complex wavelet features for iris recognition

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作者： He, Xiaofu Shi, Pengfei Department of Computer Science and Technology East China Normal University Shanghai 200241 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9781424421756

This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transform's properties of multiresolution decomposition analysis and perfect reconstruction, but also adds its new merits: approximate shift invariance, good directional selectivity for 2-D image, and limited redundancy, which are useful for iris feature extraction. So, a set of high frequency 2D-CWT coefficients are selected as features for iris recognition. The phase information of the coefficients is used for feature encoding and Hamming distance is adopted for classification. Experimental results show that the proposed algorithm can get good recognition rate. © 2008 IEEE.

关键词： Hamming distance

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A new strategy for boundary surface detection in 3D biomedical images

A new strategy for boundary surface detection in 3D biomedic...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Liuhang Cheng Lisheng Wang Yu Ma Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiaotong University Shanghai China

Detecting boundary surface for the object of interest within 3D biomedical images is an important step towards understanding and quantitative analysis of the object. However, existing 3D edge detection techniques have the limitations in detecting connected edge voxels and tracing undetected edge voxels from 3D images. In this paper, we propose a new strategy to detect boundary surface for the object of interest in 3D biomedical image. The new strategy utilizes the connectivity of boundary surface in the detection and tracing of edge voxels, and is much effective in overcoming the two limitations existing in 3D edge detectors. The performance and advantages of the proposed method are illustrated by many examples from different 3D biomedical images.

关键词： image edge detection Three dimensional displays Detectors Biomedical imaging Surface reconstruction Surface treatment Surface cracks

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Ensemble Kalman filter based on weights optimization of sampling particles

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Journal of Computational Information Systems 2014年第19期10卷 8355-8362页

作者： Hu, Zhentao Zhang, Yong Hu, Yumei Institute of Image Processing and Pattern Recognition Henan University Kaifeng China Finance Department Henan University Kaifeng China

Based on the thought of ensemble forecast, Ensemble Kalman filter (EnKF) gives a typical implementation of Bayesian estimation in Monte-Carlo simulation. However, the sampling process of particles excessively relies on the priori modeling information form the system state transition in EnKF, which inevitably causes the particle degeneracy phenomenon. In this paper, we propose a novel Ensemble Kalman filtering algorithm based on weights optimization of sampling particles. Firstly, combining with the importance-sampling technique, the contribution degree of state estimation from particles is effectively measured. Secondly, by increasing particles numbers with high weights and decreasing particles numbers with low weights, the sampling particles set is optimized in the global sense. In addition, the estimated method of importance weights on the basis of virtual observation is constructed in the framework of EnKF, and the adverse effects on the reliability and stability of particle weights caused by the observation random noise are improved. The experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： Importance sampling

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Analysis of appearance features for human matching between different fields of view

Analysis of appearance features for human matching between d...

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2009 IEEE International Conference on Multimedia and Expo, ICME 2009

作者： Xiaowei, Lv Kong, Qing-Jie Fei, Weng Yuncai, Liu Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Univ. Shanghai China Beckman Institute Department of Electrical and Computer Engineering Univ. of Illinois at Urbana-Champaign Urbana IL United States

ISBN: (纸本)9781424442911

Human matching between different fields of view is a difficult problem in intelligent video surveillance;whereas fusing multiple features has become a strong tool to solve it. In order to guide the fusion scheme, it is necessary to evaluate the matching performance of these features. In this paper, four typical features are chosen for the evaluation. They are the Color Histogram, UV Chromaticity, Major Color Spectrum Histogram, and Scale-Invariant Features (SIFT). Quantities of video data are collected to test their general accuracy, robustness, and real-time applicability. The robustness is measured under the conditions of illumination changes, Gaussian and salt noises, foreground errors, resolution changes, and camera angle differences. The experimental results show that the four features bear distinctive performances under the different conditions, which will provide important references for the feature fusion methods. ©2009 IEEE.

关键词： Graphic methods

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Pedestrian Detection in Infrared images Using HOG,LBP,Gradient Magnitude and Intensity Feature Channels

Pedestrian Detection in Infrared Images Using HOG,LBP,Gradie...

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17th International IEEE Conference on Intelligent Transportation Systems & The Asia-Pacific Council on Systems Engineering Conference 2014

作者： Raluca Brehar Sergiu Nedevschi the Image Processing and Pattern Recognition Group Computer Science DepartmentTechnical University of Cluj-Napoca

Recent work in monocular pedestrian detection is trying to improve the execution time while keeping the accuracy as high as possible.A popular and successful approach for monocular intensity pedestrian detection is based on the approximation（instead of computation） of image features for multiple scales based on the features computed on set of predefined *** port this idea to the infrared *** contributions reside in the combination of four channel features,namely infrared,histogram of gradient orientations,normalized gradient magnitude and local binary patterns with the objective of detecting pedestrians for night vision applications dealing with far infrared *** scale feature computation is done by feature *** contribution is the study of different formulations for Local Binary patterns like uniform patterns and rotation invariant patterns and their effect on detection *** detection speed is also boosted by the aid of a fast morphological based region of interest *** vary the number of approximated scales per octave and study the impact on execution time and accuracy.A reasonable result hits a speed of 18 fps with a log average miss rate of 39%.

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Learning from partially labeled data 28

Learning from partially labeled data

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28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Department of Electrical Engineering ESAT-STADIUS KU Leuven LeuvenB-3001 Belgium

ISBN: (纸本)9782875870742

Providing sufficient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications. In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among oth- ers. There are several possibilities for designing such models ranging from shallow to deep models. These type of models have received increasing in- terest due to their successful applications in real-life problems. This paper provides a brief overview of recent techniques in learning from partially labeled data. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

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A Branch-sensitive 3D Convolutional Neural Networks for Airway Segmentation

A Branch-sensitive 3D Convolutional Neural Networks for Airw...

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IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Yingjie Zhao Yusheng Liu Lisheng Wang Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781665479691

CT scans are an exceptional tool for quantitative airway analysis. Due to the complex voxel connectivity and branch structure of the airway, precise and fine segmentation results are difficult to accomplish. We propose a fully automated and end-to-end approach for airway segmentation and branch detection in chest CT based on U-Net architecture. We introduce the SE Normalization as a module for feature calibration and the hard region adaptation loss function based on cross entropy (HRA_CE) for dynamically maintaining class balance throughout the training period. We present a brand-new metric Branch DSC designed exclusively to assess the branch structure. We validate the suggested method on a dataset of 46 airway samples, and the experimental findings demonstrate that our proposed method significantly improves branch detection and segmentation continuity.

关键词： Training Measurement Three-dimensional displays Automation Computed tomography Atmospheric modeling Entropy

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Visualization methods for neural networks

Visualization methods for neural networks

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International Conference on pattern recognition

作者： H. Bischof A. Pinz W.G. Kropatsch Institute for Automation Department for Pattern Recognition and Image Processing Technical University of of Vienna Vienna Austria

The interpretation of neural network behavior is of particular interest in neural network research. Visualization methods provide the necessary means to simultaneously analyze the huge amount of information hidden in the network. The authors propose a framework for visualization methods suited for feed forward neural networks. The basic idea is to use the spatial information available outside the network to arrange the data to be visualized (weights, activations of units) in the spatial domain of the display. Several examples which illustrate the proposed framework are presented.< >

关键词： Neural networks Data visualization pattern recognition Information analysis Feedforward systems Automation image processing Computer displays Information processing Computer vision

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Word Channel Based Multiscale Pedestrian Detection Without image Resizing and Using Only One Classifier

Word Channel Based Multiscale Pedestrian Detection Without I...

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IEEE Conference on Computer Vision and pattern recognition

作者： Arthur Daniel Costea Sergiu Nedevschi Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca

ISBN: (纸本)9781479951192

Most pedestrian detection approaches that achieve high accuracy and precision rate and that can be used for real-time applications are based on histograms of gradient orientations. Usually multiscale detection is attained by resizing the image several times and by recomputing the image features or using multiple classifiers for different scales. In this paper we present a pedestrian detection approach that uses the same classifier for all pedestrian scales based on image features computed for a single scale. We go beyond the low level pixel-wise gradient orientation bins and use higher level visual words organized into Word Channels. Boosting is used to learn classification features from the integral Word Channels. The proposed approach is evaluated on multiple datasets and achieves outstanding results on the INRIA and Caltech-USA benchmarks. By using a GPU implementation we achieve a classification rate of over 10 million bounding boxes per second and a 16 FPS rate for multiscale detection in a 640×480 image.

关键词： Visualization Boosting Training Feature extraction Graphics processing units Semantics image color analysis

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