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A new semi-supervised algorithm for hyperspectral image classification based on spectral unmixing concepts

A new semi-supervised algorithm for hyperspectral image clas...

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3rd Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2011

作者： Villa, Alberto Li, Jun Plaza, Antonio Bioucas-Dias, Jose M. GIPSA-lab Signal and Image Dept. Grenoble Institute of Technology - INPG France Faculty of Electrical and Computer Engineering Iceland University of Iceland Iceland Instituto de Telecomunicações Instituto Superior Técnico TULisbon 1900-118 Lisboa Portugal Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura E-10071 Caceres Spain

Spectral unmixing is a fast growing area in hyperspectral image analysis. Many algorithms have been recently developed to retrieve pure spectral components (endmembers) and determine their abundance fractions in mixed pixels, which dominate hyperspectral images. However, possible connections between spectral unmixing concepts and classification algorithms have been rarely investigated. In this work, we propose a new method to perform semi-supervised hyperspectral image classification exploiting the information retrieved with spectral unmixing. The proposed method integrates a well-established discriminative classifier (multinomial logistic regression) with linear spectral unmixing. Furthermore, the proposed method uses a new active sampling approach which takes into account spatial context when generating new samples. The proposed method is experimentally validated using both simulated and real hyperspectral data sets. © 2011 IEEE.

关键词： Classification (of information)

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Traffic speed measurement using Spatio-Temporal model and frequency domain analysis

Traffic speed measurement using Spatio-Temporal model and fr...

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International Conference on electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Arucha Rungchokanun Vutipong Areekul Supakorn Siddhichai Hiroaki Kunieda Kasetsart Signal & Image Processing Laboratory (KSIP Lab) Department of Electrical Engineering Kasetsart University Thailand Image Technology Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Department of Communication and Integrated Systems Tokyo Institute of Technology Meguro Tokyo Japan

In traffic monitoring applications, traffic speed is an important parameter of traffic management. The method for traffic speed measurement using video based on Spatio-Temporal (ST) model and frequency domain analysis is proposed in this paper. This method is designed to be able to measure the traffic speed in every pattern of road. The novel of proposed method is unfolding the edge information on ST model and analyse them in frequency domain to determine traffic speed. The proposed method is evaluated by compare with actual speed. The traffic speed is measured accurately 97.1%.

关键词： Analytical models Sensors Pixel image sensors Monitoring

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A novel method for identification of COPD in inspiratory and expiratory states of CT images

A novel method for identification of COPD in inspiratory and...

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Middle East Conference on Biomedical engineering (MECBME)

作者： M. Parsa Hosseini Hamid Soltanian-Zadeh Shahram Akhlaghpoor Department of Electrical Engineering Islamic Azad University Tehran Iran Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Tehran University of Medical Sciences Sina Hospital Tehran Iran

Chronic obstructive pulmonary disease (COPD) refers to a group of lung diseases that block airflow and cause a huge degree of human suffering. While there is no cure for COPD and the lung damage that results in this disease cannot be reversed, it is very important to diagnose it as early as possible. Additional to diagnosis, using a mathematical model to estimate severity of disease would be helpful for evaluation of treatment effects. This paper presents a new method for identifying COPD from three-dimensional (3-D) pulmonary X-ray CT images. The method has five main steps. First, corresponding positions of lungs in inspiration and expiration are found based on anatomical structures. Then, lung regions are segmented from the CT images by active contours. Next, the left and right lungs are separated using a sequence of morphological operations. Then, parenchyma variations in each lung are found as a relationship between inspiratory and expiratory states. Finally, a classifier is used to decide about the disease and its severity. A t-test is done to evaluate the results. Twelve patients with variable severity of COPD and twelve normal adults were included in this study. The proposed method may assist radiologists in the detection of COPD as a computer aided diagnosis (CAD) system.

关键词： Lungs Computed tomography Diseases image segmentation Biomedical imaging Gaussian distribution

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Assessing lung volumetric variation to detect and stage COPD

Assessing lung volumetric variation to detect and stage COPD

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Middle East Conference on Biomedical engineering (MECBME)

Chronic obstructive pulmonary disease (COPD) is a devastating disease. In this paper, we propose a novel method for scoring of air trapping in the lungs for detection and evaluation of COPD. The proposed method finds volumetric changes of the lungs from inspiration to expiration. To this end, trachea CT images at full inspiration and expiration are compared. For this comparison, the lungs parenchyma in the inspiration and expiration images are found and changes in the surface and volume of the lungs between inspiration and expiration are used to define quantitative measures (features). Using these features, the subjects are classified into two groups of normal class and COPD patients using a Bayesian classifier. In addition, t-tests are applied to evaluate discrimination powers of the features for this classification. The main advantage of the proposed method is its ability to estimate air trapping in the lungs from CT images without human intervention. The proposed method may assists radiologists in the detection of COPD as a computer aided diagnosis (CAD) system. It may also assist them with the scoring of the disease (severity of air trapping in the lungs).

关键词： Lungs Computed tomography Diseases Active contours image segmentation Charge carrier processes

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Local color vector binary pattern for face recognition

Local color vector binary pattern for face recognition

引用

IEEE International Conference on image Processing

作者： Seung Ho Lee Jae Young Choi Konstantinos N. Plataniotis Yong Man Ro Image and Video System Laboratory Korea Advanced Institute of Science and Technology Daejeon South Korea The Edward S Rogers Sr Department of Electrical and Computer Engineering University of Toronto Toronto ONT Canada

This paper proposes local color vector binary pattern (LCVBP) as a new color based face descriptor for face recognition (FR). In the proposed LCVBP, color pixel pattern of different spectral images is extracted as discriminative information. Further, to maximize a complementary effect taken from different color channels, color angle pattern between a pair of different spectral images is incorporated into the creation of LCVBP. Experimental results on three public databases (CMU-PIE, XM2VTSDB, and Color FERET) show that the proposed color texture feature is able to significantly improve FR performance, compared to the conventional grayscale texture features including Gabor wavelet and Local Binary Pattern (LBP). In particular, the proposed LCVBP is able to provide excellent FR performance for face images taken under severe illumination variation.

关键词： image color analysis Face Feature extraction Vectors Gray-scale Histograms Face recognition

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Voxel based treatment prediction using diffusion anisotropy indices and spatial information in Glioblastoma Multiform tumor

Voxel based treatment prediction using diffusion anisotropy ...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Hadi Sabahi Hamid Soltanian-Zadeh Lisa Scarpace Tom Mikkelsen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Hospital Detroit MI USA Hermelin Brain Tumor Research Center Neurosurgery Department Henry Ford Health System Detroit MI USA

In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pre-treatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and post-treatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.

关键词： Tumors Anisotropic magnetoresistance Diffusion tensor imaging Support vector machines Feature extraction Artificial neural networks

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A comparative assessment of several processing chains for hyperspectral image classification: What features to use?

A comparative assessment of several processing chains for hy...

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Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Inmaculada Dópido Alberto Villa Antonio Plaza Paolo Gamba Hyperspectral Computing Laboratory University of Extremadura Caceres Spain GIPSA-Laboratory Signal & Image Department Grenoble Institute of Technology (INPG) France Faculty of Electrical and Computer Engineering University of Iceland Iceland Telecommunications and Remote Sensing Laboratory University of Pavia Pavia Italy

Hyperspectral image classification is a very active research area. Over the last years, several advanced feature extraction techniques have been integrated in processing chains intended for this purpose. In the context of supervised classification, the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate selection of the number of features to be used for classification purposes. This number depends on the size of the available training set, which opens the way for the incorporation of supervised techniques for feature extraction in addition to more classic unsupervised ones. In this paper, we particularly investigate the issue of how many (and what type of) features can be used effectively for SVM-based classification. For this purpose, we consider different types of feature extraction strategies - unsupervised and supervised - in the context of different types of processing chains (all based on the SVM as the baseline classifier). We also explore the role of different dimensionality estimation techniques. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and number of features needed for different analysis scenarios.

关键词： Feature extraction Training Hyperspectral imaging Estimation Support vector machines

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PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COGNITIVE RADIO SYSTEMS WITH INTERFERENCE CONSTRAINTS

PARTICLE SWARM OPTIMIZATION（PSO）OF POWER ALLOCATION IN COG...

引用

2011 4th IEEE International Conference on Broadband Network and Multimedia Technology

作者： Saeed Motiian Mohammad Aghababaie Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Colleague of EngineeringUniversity of TehranTehran 14399-57131Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences(IPM)Tehran 19548-56316 Iran Image Analysis Laboratory Radiology DepartmentHenry Ford HospitalDetroitMichigan 48202USA

Cognitive radio is used for enhancement of spectrum *** many works have been accomplished on the power allocation of cognitive radio,limited efforts have considered evolutionary *** this paper,we study this problem in the cognitive radio networks where interference constraints are defined for protection of quality of service（QoS）for both primary and secondary *** defined as functions of the signal-to-interference-plus-noise ratio（SINR） are matched for each secondary user which meets Nash’s *** general,the region of utilities that meets the constraints is *** is possible to make simplifications,generate a convex region,and then use common convex optimization approaches to obtain a ***,Particle Swarm Optimization（PSO）does not need such simplifications and thus its results are superior to those of the convex optimization *** is an evolutionary algorithm based on social intelligence,utilized in many optimization *** is a global optimizations algorithm that does not require the objective function be differentiable as required in classic optimization methods.

关键词： Cognitive radio Power allocation Particle Swarm Optimization（PSO）.

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Supervised Learning Framework For Screening Nuclei in Tissue Sections

Supervised Learning Framework For Screening Nuclei in Tissue...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society

作者： Kaustav Nandy Prabhakar R. Gudla Ryan Amundsen Karen J. Meaburn Tom Misteli Stephen J. Lockett Optical Microscopy and Analysis Laboratory Advanced Technology program SAIC-Frederick Inc. Electrical Engineering and Computer Science Department University of Michigan Cell Biology of Gene Expression National Cancer Institute National Institutes of Health

ISBN: (纸本)9781424441211

Accurate segmentation of cell nuclei in microscope images of tissue sections is a key step in a number of biological and clinical applications. Often such applications require analysis of large image datasets for which manual segmentation becomes subjective and time consuming. Hence automation of the segmentation steps using fast, robust and accurate image analysis and pattern classification techniques is necessary for high throughput processing of such datasets. We describe a supervised learning framework, based on artificial neural networks (ANNs), to identify well-segmented nuclei in tissue sections from a multistage watershed segmentation algorithm. The successful automation was demonstrated by screening over 1400 well segmented nuclei from 9 datasets of human breast tissue section images and comparing the results to a previously used stacked classifier based analysis framework.

关键词： tissue section Cell nucleus Semi-supervised Learning Nucleus Dataset image analysis MULTISTAGE Artificial neural networks Frameworks nuclei (nuclear physics) segmentation Segmentation Automation pattern classification method

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A deformable surface model based automatic rat brain extraction method

A deformable surface model based automatic rat brain extract...

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IEEE International Symposium on Biomedical Imaging

作者： Jiehua Li Xiaofeng Liu Jiachen Zhuo Rao P. Gullapalli Jason M. Zara Department of Electrical and Computer Engineering George Washington University Washington D.C. DC USA General Electric Global Research Center Visualization and Computer Vision Laboratory Niskayuna NY USA Department of Radiology University of Maryland School of Medicine Baltimore MD USA University of Maryland School of Law Baltimore MD US

ISBN: (纸本)9781424441273

The extraction of the brain portion of a neurological image is often necessary prior to tissue segmentation or image registration. While MR Imaging studies on the rat have gained much interest lately, an automatic and robust rat brain extraction tool is still lacking. In this paper, we present a deformable surface model-based rat brain extraction method which extends the popular human brain extraction tool (BET) by incorporating the brain geometry and MRI tissue characteristics of the rat into consideration for more robust extraction. Our method was demonstrated on T2-weighted MR images for five rats and compared with other rat brain extraction methods. Results showed that our method can reliably and robustly extract the rat brain with high accuracy (>;92% volume overlap).

关键词： Brain modeling Humans Force Surface roughness Rough surfaces Geometry

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