检索结果-内蒙古大学图书馆

IEEE International Symposium on biomedical Imaging

作者： Robert F. Murphy Ray and Stephanie Lane Center for Computational Biology Center for Bioimage Informatics Department of Biological Sciences Biomedical Engineering and Machine Learning Carnegie Mellon University Pittsburgh PA USA

A major source of information for identifying subcellular location on a proteome-wide basis will be imaging of tagged proteins in living cells using fluorescence microscopy. We have previously developed automated systems to interpret images from such experiments and demonstrated that they can perform as well or better than visual inspection. Recent work demonstrates that these methods can be applied to large collections of images from sources as diverse as yeast expressing GFP-tagged proteins and human tissues imaged by immunocytochemistry. A distinct but related task is learning what location patterns exist. We have demonstrated clustering of mouse proteins into subcellular location families that share a statistically indistinguishable pattern. To communicate each pattern, we have developed approaches to learning generative models of subcellular patterns. Integration of high-throughput microscopy and automated model building with cell modeling systems will permit accurate, well-structured information on subcellular location to be incorporated into systems biology efforts.

关键词： Throughput Microscopy Proteins Biological system modeling Information resources Fluorescence Inspection Fungi Humans Immune system

来源：评论

学校读者我要写书评

暂无评论

Axonal bouton modeling, detection and distribution analysis for the study of neural circuit organization and plasticity

Axonal bouton modeling, detection and distribution analysis ...

引用

IEEE International Symposium on biomedical Imaging

作者： Christina A. Hallock Inci Ozgunes Ramamurthy Bhagavatula Gustavo K. Rohde Justin C. Crowley Christina E. Onorato Abhay Mavalankar Amina Chebira Chuen Hwa Tan Markus Puschel Jelena Kovacevic Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Department of Biomedical Engineering and Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Department of Biological Sciences and Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh PA USA Department of Electrical and Compute Engineeringr Carnegie Mellon University Pittsburgh PA USA

We propose a novel method for axonal bouton modeling and automated detection in populations of labeled neurons, as well as bouton distribution analysis for the study of neural circuit organization and plasticity. Since axonal boutons are the presynaptic specializations of neural synapses, their locations can be used to determine the organization of neural circuitry, and in time-lapse studies, neural circuit dynamics. We propose simple geometric models for axonal boutons that account for variations in size, position, rotation and curvature of the axon in the vicinity of the bouton. We then use the normalized cross-correlation between the model and image data as a test statistic for bouton detection and position estimation. Thus, the problem is cast as a statistical detection problem where we can tune the algorithm parameters to maximize the probability of detection for a given probability of false alarm. For example, we can detect 81% of boutons with 9% false alarm from noisy, out of focus, images. We also present a novel method to characterize the orientation and elongation of a distribution of labeled boutons and we demonstrate its performance by applying it to a labeled data set.

关键词： Circuits Nerve fibers Microscopy Neurons Neuroscience Data mining biomedical engineering Probability Image resolution Neurotransmitters

来源：评论

学校读者我要写书评

暂无评论

Lapped Tight Frame Transforms

Lapped Tight Frame Transforms

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Amina Chebira Jelena Kovacevic Department of Biomedical Engineering and Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Dept. of Biomedical Eng. and Center for Bioimage Informatics Dept. of Electrical and Computer Eng. Carnegie Mellon University Pittsburgh PA. Email: jelenak@cmu.edu

We propose a new class of equal-norm tight frames termed lapped tight frame transforms (LTFTs). These can be seen as a redundant counterpart to bases known as lapped orthogonal transforms (LOTs) introduced by Malvar and Cassereau, as well as an infinite-dimensional counterpart to harmonic tight frames (HTFs). To construct LTFTs, we seed them from LOTs and show that, in a specific case, the process preserves the equal norm. As both their basis counterpart LOTs as well as their finite-dimensional one HTFs, LTFTs possess many desirable properties, such as equal norm and efficient implementation.

关键词： Filter bank Power harmonic filters Signal processing Signal representations Robustness biomedical informatics biomedical computing Wavelet transforms Discrete cosine transforms Channel bank filters

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Multiresolution Approach to Fingerprint Recognition

An Adaptive Multiresolution Approach to Fingerprint Recognit...

引用

IEEE International Conference on Image Processing

作者： Amina Chebira Luis P. Coelho Aliaksei Sandryhaila Stephen Lin William G. Jenkinson Jeremiah MacSleyne Christopher Hoffman Philipp Cuadra Charles Jackson Markus Puschel Jelena Kovacevic Department of Biomedical Engineering and Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Carnegie Mellon University Pittsburgh PA USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Department of Material Sciences and Engineering Carnegie Mellon University Pittsburgh PA USA Dept. of Biomedical Eng. and Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Dept. of Electrical and Computer Eng. Carnegie Mellon University Pittsburgh PA USA

We propose an adaptive multiresolution (MR) approach to the classification of fingerprint images. The system adds MR decomposition in front of a generic classifier consisting of feature computation and classification in each MR subspace, yielding local decisions, which are then combined into a global decision using a weighting algorithm. In our previous work on classification of protein subcellular location images, we showed that the space-frequency localized information in the MR subspaces adds significantly to the discriminative power of the system. Here, we go one step farther; We develop a new weighting method which allows for the discriminative power of each subband to be expressed and examined within each class. This, in turn, allows us to evaluate the importance of the information contained within a specific subband. Moreover, we develop a pruning procedure to eliminate the subbands that do not contain useful information. This leads to potential identification of the appropriate MR decomposition both on a per class basis and for a given dataset. With this new approach, we make the system adaptive, flexible as well as more accurate and efficient.

关键词： Fingerprint recognition Image matching Energy resolution Image resolution Biometrics Biology computing Wavelet packets biomedical informatics biomedical computing Biological materials

来源：评论

学校读者我要写书评

暂无评论

Efficient Acquisition and Learning of Fluorescence Microscope Data Models

Efficient Acquisition and Learning of Fluorescence Microscop...

引用

IEEE International Conference on Image Processing

作者： Charles Jackson Robert F. Murphy Jelena Kovacevic Department of Biomedical Engineering and Center for Bioimage Informatics Carnegie Mellon University Pittsburgh PA USA Department of Biological Sciences and Department of Machine Learning Carnegie Mellon University Pittsburgh PA USA Carnegie Mellon University Pittsburgh PA US

We present a method for efficient acquisition of fluorescence microscope datasets, to allow for higher spatial and temporal resolution, and with less damage from photobleaching. Our proposal is to restrict acquisition to regions where we expect to find an object. Given that the objects are continuously moving, we must have an accurate model to describe objects' motion to predict their future locations. We outline a system for learning and applying this motion model, provide details from some simple simulations, and summarize results from more complex applications.

关键词： Fluorescence Microscopy Data models State-space methods Photobleaching Machine learning Tracking Equations Spatial resolution Biological system modeling

来源：评论

学校读者我要写书评

暂无评论

A Novel Graphical Model Approach to Segmenting Cell Images

A Novel Graphical Model Approach to Segmenting Cell Images

引用

2006 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2006)

作者： Shann-Ching Chen Ting Zhao Geoffrey J. Gordon Robert F. Murphy Center for Bioimage Informatics Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Center for Biomage Informatics Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Center for Biomage Informatics Department of Machine Learning Carnegie Mellon University Pittsburgh PA USA Department of Machine Learning and Carnegie Mellon University Pittsburgh PA USA

Successful biological image analysis usually requires satisfactory segmentations to identify regions of interest as an intermediate step. Here we present a novel graphical model approach for segmentation of multi-cell yeast images acquired by fluorescence microscopy. Yeast cells are often clustered together, so they are hard to segment by conventional techniques. Our approach assumes that two parallel images are available for each field: an image containing information about the nuclear positions (such as an image of a DNA probe) and an image containing information about the cell boundaries (such as a differential interference contrast, or DIC, image). The nuclear information provides an initial assignment of whether each pixel belongs to the background or one of the cells. The boundary information is used to estimate the probability that any two pixels in the graph are separated by a cell boundary. From these two kinds of information, we construct a graph that links nearby pairs of pixels, and seek to infer a good segmentation from this graph. We pose this problem as inference in a Bayes network, and use a fast approximation approach to iteratively improve the estimated probability of each class for each pixel. The resulting algorithm can efficiently generate segmentation masks which are highly consistent with hand-labeled data, and results suggest that the work will be of particular use for large scale determination of protein location patterns by automated microscopy.

关键词： Graphical models Image segmentation Fungi Microscopy Image analysis Fluorescence DNA Probes Interference Inference algorithms

来源：评论

学校读者我要写书评

暂无评论

Adaptive multirate data acquisition of 3D cell images

Adaptive multirate data acquisition of 3D cell images

引用

2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05

作者： Merryman, T.E. Kovačević, J. Osuna, E. Garcia Murphy, R.F. Departments of Electrical and Computer Engineering Carnegie Mellon University Center for Bioimage Informatics 5000 Forbes Ave. Pittsburgh PA 15213 United States Departments of Biomedical Engineering Carnegie Mellon University Center for Bioimage Informatics 5000 Forbes Ave. Pittsburgh PA 15213 United States Departments of Biological Sciences Carnegie Mellon University Center for Bioimage Informatics 5000 Forbes Ave. Pittsburgh PA 15213 United States

ISBN: (纸本)0780388747

We present an algorithm for efficient acquisition of fluorescence microscopy data sets, a problem not addressed until now in the literature. We do this as part of a larger system for protein classification based on their subcellular location patterns, and thus strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality;we have access only to the information that has been scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive multiresolution scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to increase either time of space resolution of our data set, all while minimally affecting the classification accuracy of the entire system. © 2005 IEEE.

关键词： Image processing

来源：评论

学校读者我要写书评

暂无评论

Adaptive multirate data acquisition of 3D cell images

Adaptive multirate data acquisition of 3D cell images

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： T.E. Merryman J. Kovacevic E.G. Osuna R.F. Murphy Center for Bioimage Informatics Departments of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Departments of Electr. & Comput. Eng. Carnegie Mellon Univ. Pittsburgh PA USA Center for Bioimage Informatics Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Departments of Electrical and Computer Engineering Biological Sciences Carnegie Mellon University Pittsburgh PA USA

We present an algorithm for efficient acquisition of fluorescence microscopy data sets, a problem not addressed until now in the literature. We do this as part of a larger system for protein classification based on their subcellular location patterns, and thus strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality; we have access only to the information that has been scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive multiresolution scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to increase either time or space resolution of our data set, all while minimally affecting the classification accuracy of the entire system.

关键词： Data acquisition Microscopy Image coding Fluorescence Rate-distortion Image resolution Pixel Proteins biomedical informatics biomedical engineering

来源：评论

学校读者我要写书评

暂无评论

Contributory presentations/posters

引用

Journal of Biosciences 1999年第1期24卷 33-198页

作者： N. Manoj V. R. Srinivas A. Surolia M. Vijayan K. Suguna R. Ravishankar R. Schwarzenbacher K. Zeth Diederichs G. M. Kostner A. Gries P. Laggner R. Prassl Madhusudan Pearl Akamine Nguyen-huu Xuong Susan S. Taylor M. Bidva Sagar K. Saikrishnan S. Roy K. Purnapatre P. Handa U. Varshney B. K. Biswal N. Sukumar J. K. Mohana Rao A. Johnson Vasantha Pattabhi S. Sri Krishna Mira Sastri H. S. Savithri M. R. N. Murthy Bindu Pillai Kannan M. V. Hosur Mukesh Kumar Swati Patwardhan K. K. Kannan B. Padmanabhaa S. Sasaki-Sugio M. Nukaga T. Matsuzaki S. Karthikevan S. Sharma A. K. Sharma M. Paramasivam P. Kumar J. A. Khan S. Yadav A. Srinivasan T. P. Singh S. Gourinath Neelima Alam A. Srintvasan Vikas Chandra Punit Kaur Ch. Betzel S. Ghosh A. K. Bera S. Bhattacharya S. Chakraborty A. K. Pal B. P. Mukhopadhyay I. Dey U. Haldar Asok Baneriee Jozef Sevcik Adriana Solovicova K. Sekar M. Sundaralingam N. Genov Dong-cai Liang Tao Jiang Ji-ping Zhang Wen-rui Chang Wolfgang Jahnke Marcel Blommers S. C. Panchal R. V. Hosur Bindu Pillay Puniti Mathur S. Srivatsun Ratan Mani Joshi N. R. Jaganathan V. S. Chauhan H. S. Atreya S. C. Sahu K. V. R. Chary Girjesh Govil Elisabeth Adjadj Éric Quinjou Nadia Izadi-Pruneyre Yves Blouquit Joël Mispelter Bernadette Heyd Guilhem Lerat Philippe Milnard Michel Desmadreil Y. Lin B. D. Nageswara Rao Vidva Raghunathan Mei H. Chau Prashant Pesais Sudha Srivastava Evans Coutinho Anil Saran Leizl F. Sapico Jayson Gesme Herbert Lijima Raymond Paxton Thamarapu Srikrishnan C. R. Grace G. Nagenagowda A. M. Lynn Sudha M. Cowsik Sarata C. Sahu S. Chauhan A. Bhattacharya G. Govil Anil Kumar Maurizio Pellecchia Erik R. P. Zuiderweg Keiichi Kawano Tomoyasu Aizawa Naoki Fujitani Yoichi Hayakawa Atsushi Ohnishi Tadayasu Ohkubo Yasuhiro Kumaki Kunio Hikichi Katsutoshi Nitta V. Rani Parvathy R. M. Kini Takumi Koshiba Yoshihiro Kobashigawa Min Yao Makoto Demura Astushi Nakagawa Isao Tanaka Kunihiro Kuwajima Jens Linge Seán O. Donoghue Michael Nilges G. Chakshusmathi Girish S. Ratnaparkhi P. K. Madhu R. Varadarajan C. Tetreau Molecular Biophysics Unit Indian Institute of Science Bangalore India Austrian Academy of Sciences Institute of Biophysics and X-ray Structure Research Graz Austria Faculty of Biology University of Konstanz Germany Institute of Medical Biochemistry University of Graz Austria Howard Huges Medical Institute Department of Chemistry & Biochemistry University of California La Jolla USA Department of Chemistry & Biochemistry University of California San Diego La Jolla USA Molecular Biophysics Unit India Department of Microbiology &Cell Biology Indian Institute of Science Bangalore India Macromolecular Structure Laboratory NCI - Frederick Cancer Research and Development Center ABL - Basic Research Program Frederick USA Department of Crystallography and Biophysics University of Madras Chennai Biochemistry Department Indian Institute of Science Bangalore India Condensed Matter Physics Division B.A.R.C. Trombay Mumbai Condensed Matter Physics Division Bhabha Atomic Research Centre Trombay Mumbai IMCB The University of Tokyo Tokyo Japan Mitsubishi Chemical Corporation Yokohama Research Center Yokohama Japan Faculty of Pharmaceutical Sciences Chiba University Chiba Japan Department of Biophysics All India Institute of Medical Sciences New Delhi India Department of Biophysics All India Institute of Medical sciences New Delhi India Institüt Physiologische Chemie Hamburg Germany Biophysics Department Bose Institute Calcutta India Slovak Academy of Sciences Institute of Molecular Biology Bratislava Slovakia Indian Institute of Science Bioinformatics Centre Bangalore Departments of Chemistry and Biochemistry Biological Macromolecular Structure Center USA Bulgarian Academy of Sciences Institute of Organic Chemistry Sofia Bulgaria Chinese Academy of Sciences Institute of Biophysics Beijing China Novartis Pharma AG Preclinical Research Basel Switzerland Department of Chemical Sciences Tata Institute of Fundamental Research Mumbai India Solid State Physics Div

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：