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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Fawzi, Alhussein Tosic, Tamara Frossard, Pascal Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland

ISBN: (纸本)9781467300469

We consider the problem of recovering a set of correlated signals (e. g., images from different viewpoints) from a few linear measurements per signal. We assume that each sensor in a network acquires a compressed signal in the form of linear measurements and sends it to a joint decoder for reconstruction. We propose a novel joint reconstruction algorithm that exploits correlation among underlying signals. Our correlation model considers geometrical transformations between the supports of the different signals. The proposed joint decoder estimates the correlation and reconstructs the signals using a simple thresholding algorithm. We give both theoretical and experimental evidence to show that our method largely outperforms independent decoding in terms of support recovery and reconstruction quality.

关键词： Distributed compressed sensing thresholding algorithm Sparse signals Overcomplete dictionary

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Assessing statistical significance in causal graphs

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BMC BIOINFORMATICS 2012年第1期13卷 1-14页

作者： Chindelevitch, Leonid Loh, Po-Ru Enayetallah, Ahmed Berger, Bonnie Ziemek, Daniel Pfizer Worldwide Res & Dev Computat Sci Ctr Emphasis Cambridge MA 02142 USA MIT Dept Math Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA Pfizer Worldwide Res & Dev Compound Safety Predict Cambridge MA USA

Background: Causal graphs are an increasingly popular tool for the analysis of biological datasets. In particular, signed causal graphs-directed graphs whose edges additionally have a sign denoting upregulation or downregulation-can be used to model regulatory networks within a cell. Such models allow prediction of downstream effects of regulation of biological entities;conversely, they also enable inference of causative agents behind observed expression changes. However, due to their complex nature, signed causal graph models present special challenges with respect to assessing statistical significance. In this paper we frame and solve two fundamental computational problems that arise in practice when computing appropriate null distributions for hypothesis testing. Results: First, we show how to compute a p-value for agreement between observed and model-predicted classifications of gene transcripts as upregulated, downregulated, or neither. Specifically, how likely are the classifications to agree to the same extent under the null distribution of the observed classification being randomized? This problem, which we call "Ternary Dot Product Distribution" owing to its mathematical form, can be viewed as a generalization of Fisher's exact test to ternary variables. We present two computationally efficient algorithms for computing the Ternary Dot Product Distribution and investigate its combinatorial structure analytically and numerically to establish computational complexity bounds. Second, we develop an algorithm for efficiently performing random sampling of causal graphs. This enables p-value computation under a different, equally important null distribution obtained by randomizing the graph topology but keeping fixed its basic structure: connectedness and the positive and negative in-and out-degrees of each vertex. We provide an algorithm for sampling a graph from this distribution uniformly at random. We also highlight theoretical challenges unique to signed causa

关键词： Graph Randomization Gene Expression Dataset Causal Network thresholding algorithm Causal Graph

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GiA Roots: software for the high throughput analysis of plant root system architecture

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BMC PLANT BIOLOGY 2012年第1期12卷 1-12页

作者： Galkovskyi, Taras Mileyko, Yuriy Bucksch, Alexander Moore, Brad Symonova, Olga Price, Charles A. Topp, Christopher N. Iyer-Pascuzzi, Anjali S. Zurek, Paul R. Fang, Suqin Harer, John Benfey, Philip N. Weitz, Joshua S. Georgia Inst Technol Sch Biol Atlanta GA 30332 USA Duke Univ Dept Math Durham NC 27706 USA Georgia Inst Technol Sch Interact Comp Atlanta GA 30332 USA Duke Univ Dept Biol Durham NC USA Inst Sci & Technol Vienna Austria Univ Western Australia Dept Plant Biol Perth WA 6009 Australia Duke Univ Duke Ctr Syst Biol Durham NC USA S China Agr Univ Root Biol Ctr Guangzhou Guangdong Peoples R China Georgia Inst Technol Sch Phys Atlanta GA 30332 USA

Background: Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks. Results: We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user. Conclusions: We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

关键词： Processing Pipeline Root System Architecture Root Crown Adaptive thresholding thresholding algorithm

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A thresholding approach for detection of sputum cell for lung cancer early diagnosis

A thresholding approach for detection of sputum cell for lun...

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IET Conference on Image Processing (IPR 2012)

作者： Fatma Taher Naoufel Werghi Hussain Al-Ahmad Department of Electronics and Computer Engineering Khalifa University Sharjah UAE

ISBN: (纸本)9781622762675

In this paper, we address the problem of detection and extraction of sputum cells that help in lung cancer early diagnosis. Our approach is based on thresholding classifier looking at the distribution of sputum pixels and non sputum pixels in RGB space for extracting the sputum cell from the raw sputum image. In this method the problem is viewed as a segmentation problem focusing on extraction of such sputum cells from the images whereby we want to partition the image into sputum cell regions including the nuclei, cytoplasm and the background that includes all the rest. These cells will be analyzed to check whether they are cancerous or not. In this study, we used a database of 100 sputum color images to test the thresholding classifier by comparing it with the ground truth data of extracted sputum cells and it has shown a better extraction result than previous work. Moreover, we computed a histogram for different color spaces (RGB, YCbCr, HSV, L*a*b* and XYZ) to find the best color space with low false detection rate. We used some performance criteria such as precision, specificity and accuracy to evaluate the improved thresholding classifier.

关键词： Lung Cancer Detection Image Segmentation Sputum Cells thresholding algorithm Sputum Lung Cancer color spaces Performance metrics images Classifiers

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Comparison of threshold algorithms for automatic image processing of rice roots using freeware ImageJ

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FIELD CROPS RESEARCH 2011年第3期121卷 460-463页

作者： Tajima, Ryosuke Kato, Yoichiro Tohoku Univ Field Sci Ctr Ohsaki Miyagi 9896711 Japan Univ Tokyo Inst Sustainable Agroecosyst Serv Tokyo 1880002 Japan

High-throughput phenotyping of crop root system architecture using digital image analysis requires costly special software (WinRHIZO). Novel freeware, ImageJ, has also been developed for the similar purpose, but the presetting of the threshold to create root binary images makes this method time-consuming. This study compares the 16 algorithms available in the ImageJ for processing of rice root images. Among the algorithms, the Triangle algorithm proved to be the best binary method, where the coefficient of correlation between ImageJ-estimated and WinRHIZO-estimated root lengths is extremely high (r = 0.986). However, using the Triangle algorithm, ImageJ overestimated the rice root length compared with WinRHIZO. By multiplying the values obtained using ImageJ by 2/3, the estimates closely corresponded to those estimated by WinRHIZO. The correspondence of root lengths estimated using WinRHIZO and ImageJ with Triangle algorithm was valid for roots of various morphologies, and for rice plants grown in uplands, rainfed lowlands, and irrigated lowlands. This report proposes the completely automated estimation of rice root length using freeware ImageJ with the appropriate threshold algorithm for image processing. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Root length Image analysis lmageJ WinRHIZO thresholding algorithm

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Coherence-Based Performance Guarantees for Estimating a Sparse Vector Under Random Noise

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2010年第10期58卷 5030-5043页

作者： Ben-Haim, Zvika Eldar, Yonina C. Elad, Michael Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

We consider the problem of estimating a deterministic sparse vector x(0) from underdetermined measurements Ax(0) + w, where w represents white Gaussian noise and A is a given deterministic dictionary. We provide theoretical performance guarantees for three sparse estimation algorithms: basis pursuit denoising (BPDN), orthogonal matching pursuit (OMP), and thresholding. The performance of these techniques is quantified as the l(2) distance between the estimate and the true value of x(0). We demonstrate that, with high probability, the analyzed algorithms come close to the behavior of the oracle estimator, which knows the locations of the nonzero elements in x(0). Our results are non-asymptotic and are based only on the coherence of A, so that they are applicable to arbitrary dictionaries. This provides insight on the advantages and drawbacks of l(1) relaxation techniques such as BPDN and the Dantzig selector, as opposed to greedy approaches such as OMP and thresholding.

关键词： Basis pursuit Dantzig selector matching pursuit oracle sparse estimation thresholding algorithm

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COHERENCE-BASED NEAR-ORACLE PERFORMANCE GUARANTEES FOR SPARSE ESTIMATION UNDER GAUSSIAN NOISE

COHERENCE-BASED NEAR-ORACLE PERFORMANCE GUARANTEES FOR SPARS...

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2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Ben-Haim, Zvika Eldar, Yonina C. Elad, Michael Technion Israel Inst Technol IL-32000 Haifa Israel

ISBN: (纸本)9781424442966

关键词： Sparse estimation basis pursuit matching pursuit thresholding algorithm oracle

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COHERENCE-BASED NEAR-ORACLE PERFORMANCE GUARANTEES FOR SPARSE ESTIMATION UNDER GAUSSIAN NOISE

COHERENCE-BASED NEAR-ORACLE PERFORMANCE GUARANTEES FOR SPARS...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zvika Ben-Haim Yonina C. Eldar Michael Elad Technion--Israel Institute of Technology Haifa 32000 Israel

ISBN: (纸本)9781424442959

We consider the problem of estimating a deterministic sparse vector x_(0) from underdetermined measurements Ax_(0) + w, where w represents white Gaussian noise and A is a given deterministic dictionary. We analyze the performance of three sparse estimation algorithms: basis pursuit denoising, orthogonal matching pursuit, and thresholding. These approaches are shown to achieve near-oracle performance with high probability, assuming that x_(0) is sufficiently sparse. Our results are non-asymptotic and are based only on the coherence of A, so that they are applicable to arbitrary dictionaries.

关键词： Sparse estimation basis pursuit matching pursuit thresholding algorithm oracle matching pursuits Information retrieval systems-Teletext and videotex Gaussian noise Dictionaries as Topic Gaussian white noise coherence

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An improved wavelet-based speech enhancement by using speech signal features

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COMPUTERS & ELECTRICAL ENGINEERING 2006年第6期32卷 411-425页

作者： Ayat, Saeed Manzuri-Shalmani, M. T. Dianat, Roohollah Sharif Univ Technol Dept Comp Engn Tehran Iran

This paper presents a new speech enhancement system that works in wavelet domain. The core of system is an improved WaveShrink module. First, different parameters of WaveShrink are studied;then, based on the features of speech signal, an improved wavelet-based speech enhancement system is proposed. The system uses a novel thresholding algorithm, and introduces a new method for threshold selection. Moreover, the efficiency of system has been increased by selecting more suitable parameters for voiced, unvoiced and silence regions, separately. The proposed system has been evaluated on different sentences under various noise conditions. The results show a plausible improvement in performance of system, in comparison with similar approaches. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： speech enhancement WaveShrink threshold selection shrink function thresholding algorithm

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Nonlinear approximation with dictionaries I. Direct estimates

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JOURNAL OF FOURIER ANALYSIS AND APPLICATIONS 2004年第1期10卷 51-71页

作者： Gribonval, R Nielsen, M IRISA INRIA F-35042 Rennes France Aalborg Univ Dept Math Sci DK-9200 Aalborg Denmark

We study various approximation classes associated with m-term approximation by elements from a (possibly redundant) dictionary in a Banach space. The standard approximation class associated with the best m-term approximation is compared to new classes defined by considering m-term approximation with algorithmic constraints: thresholding and Chebychev approximation classes are studied, respectively. We consider embeddings of the Jackson type (direct estimates) of sparsity spaces into the mentioned approximation classes. General direct estimates are based on the geometry of the Banach space, and we prove that assuming a certain structure Of the dictionary is sufficient and (almost) necessary to obtain stronger results. We give examples of classical dictionaries in L-p spaces and modulation spaces where our results recover some known Jackson type estimates, and discuss some new estimates they provide.

关键词： nonlinear m-term approximation dictionary constrained approximation thresholding algorithm greedy algorithm sparse decomposition Jackson inequality Hilbertian property

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