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Management of Large Sets of Image Data Capture, Databases, Image Processing, Storage, Visualization Karol Kozak
Management of large sets of image data Capture, Databases, Image Processing, Storage, Visualization Karol Kozak Download free books at Karol Kozak Management of large sets of image data Capture, Databases, Image Processing, Storage, Visualization Download free eBooks at bookboon.com 2 Management of large sets of image data: Capture, Databases, Image Processing, Storage, Visualization 1st edition © 2014 Karol Kozak & bookboon.com ISBN 978-87-403-0726-9 Download free eBooks at bookboon.com 3 Management of large sets of image data Contents Contents 1 Digital image 6 2 History of digital imaging 10 3 Amount of produced images – is it danger? 18 4 Digital image and privacy 20 5 Digital cameras 27 5.1 Methods of image capture 31 6 Image formats 33 7 Image Metadata – data about data 39 8 Interactive visualization (IV) 44 9 Basic of image processing 49 Download free eBooks at bookboon.com 4 Click on the ad to read more Management of large sets of image data Contents 10 Image Processing software 62 11 Image management and image databases 79 12 Operating system (os) and images 97 13 Graphics processing unit (GPU) 100 14 Storage and archive 101 15 Images in different disciplines 109 15.1 Microscopy 109 360° 15.2 Medical imaging 114 15.3 Astronomical images 117 15.4 Industrial imaging 360° 118 thinking. 16 Selection of best digital images 120 References: thinking. 124 360° thinking . 360° thinking. Discover the truth at www.deloitte.ca/careers Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities. Discover the truth at www.deloitte.ca/careers © Deloitte & Touche LLP and affiliated entities. -
Bioimage Analysis Tools
Bioimage Analysis Tools Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, Ulrike Schulze, Simon Norrelykke, Christian Tischer, Thomas Pengo To cite this version: Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Paul-Gilloteaux, et al.. Bioimage Analysis Tools. Kota Miura. Bioimage Data Analysis, Wiley-VCH, 2016, 978-3-527-80092-6. hal- 02910986 HAL Id: hal-02910986 https://hal.archives-ouvertes.fr/hal-02910986 Submitted on 3 Aug 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 2 Bioimage Analysis Tools 1 2 3 4 5 6 Kota Miura, Sébastien Tosi, Christoph Möhl, Chong Zhang, Perrine Pau/-Gilloteaux, - Ulrike Schulze,7 Simon F. Nerrelykke,8 Christian Tischer,9 and Thomas Penqo'" 1 European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany National Institute of Basic Biology, Okazaki, 444-8585, Japan 2/nstitute for Research in Biomedicine ORB Barcelona), Advanced Digital Microscopy, Parc Científic de Barcelona, dBaldiri Reixac 1 O, 08028 Barcelona, Spain 3German Center of Neurodegenerative -
Cellanimation: an Open Source MATLAB Framework for Microscopy Assays Walter Georgescu1,2,3,∗, John P
Vol. 28 no. 1 2012, pages 138–139 BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btr633 Systems biology Advance Access publication November 24, 2011 CellAnimation: an open source MATLAB framework for microscopy assays Walter Georgescu1,2,3,∗, John P. Wikswo1,2,3,4,5 and Vito Quaranta1,3,6 1Vanderbilt Institute for Integrative Biosystems Research and Education, 2Department of Biomedical Engineering, Vanderbilt University, 3Center for Cancer Systems Biology at Vanderbilt, Vanderbilt University Medical Center, Nashville, TN, USA, 4Department of Molecular Physiology and Biophysics, 5Department of Physics and Astronomy, Vanderbilt University and 6Department of Cancer Biology, Vanderbilt University Medical Center, Nashville, TN, USA Associate Editor: Jonathan Wren ABSTRACT 1 INTRODUCTION Motivation: Advances in microscopy technology have led to At present there are a number of microscopy applications on the the creation of high-throughput microscopes that are capable of market, both open-source and commercial, and addressing both generating several hundred gigabytes of images in a few days. very specific and broader microscopy needs. In general, commercial Analyzing such wealth of data manually is nearly impossible and software packages such as Imaris® and MetaMorph® tend to requires an automated approach. There are at present a number include more complete sets of algorithms, covering different kinds of open-source and commercial software packages that allow the of experimental setups, at the cost of ease of customization and user -
Abstractband
Volume 23 · Supplement 1 · September 2013 Clinical Neuroradiology Official Journal of the German, Austrian, and Swiss Societies of Neuroradiology Abstracts zur 48. Jahrestagung der Deutschen Gesellschaft für Neuroradiologie Gemeinsame Jahrestagung der DGNR und ÖGNR 10.–12. Oktober 2013, Gürzenich, Köln www.cnr.springer.de Clinical Neuroradiology Official Journal of the German, Austrian, and Swiss Societies of Neuroradiology Editors C. Ozdoba L. Solymosi Bern, Switzerland (Editor-in-Chief, responsible) ([email protected]) Department of Neuroradiology H. Urbach University Würzburg Freiburg, Germany Josef-Schneider-Straße 11 ([email protected]) 97080 Würzburg Germany ([email protected]) Published on behalf of the German Society of Neuroradiology, M. Bendszus president: O. Jansen, Heidelberg, Germany the Austrian Society of Neuroradiology, ([email protected]) president: J. Trenkler, and the Swiss Society of Neuroradiology, T. Krings president: L. Remonda. Toronto, ON, Canada ([email protected]) Clin Neuroradiol 2013 · No. 1 © Springer-Verlag ABC jobcenter-medizin.de Clin Neuroradiol DOI 10.1007/s00062-013-0248-4 ABSTRACTS 48. Jahrestagung der Deutschen Gesellschaft für Neuroradiologie Gemeinsame Jahrestagung der DGNR und ÖGNR 10.–12. Oktober 2013 Gürzenich, Köln Kongresspräsidenten Prof. Dr. Arnd Dörfler Prim. Dr. Johannes Trenkler Erlangen Wien Dieses Supplement wurde von der Deutschen Gesellschaft für Neuroradiologie finanziert. Inhaltsverzeichnis Grußwort ............................................................................................................................................................................ -
A Flexible Image Segmentation Pipeline for Heterogeneous
A exible image segmentation pipeline for heterogeneous multiplexed tissue images based on pixel classication Vito Zanotelli & Bernd Bodenmiller January 14, 2019 Abstract Measuring objects and their intensities in images is basic step in many quantitative tissue image analysis workows. We present a exible and scalable image processing pipeline tai- lored to highly multiplexed images. This pipeline allows the single cell and image structure segmentation of hundreds of images. It is based on supervised pixel classication using Ilastik to the distill the segmentation relevant information from the multiplexed images in a semi- supervised, automated fashion, followed by standard image segmentation using CellProler. We provide a helper python package as well as customized CellProler modules that allow for a straight forward application of this workow. As the pipeline is entirely build on open source tool it can be easily adapted to more specic problems and forms a solid basis for quantitative multiplexed tissue image analysis. 1 Introduction Image segmentation, i.e. division of images into meaningful regions, is commonly used for quan- titative image analysis [2, 8]. Tissue level comparisons often involve segmentation of the images in macrostructures, such as tumor and stroma, and calculating intensity levels and distributions in such structures [8]. Cytometry type tissue analysis aim to segment the images into pixels be- longing to the same cell, with the goal to ultimately identify cellular phenotypes and celltypes [2]. Classically these approaches are mostly based on a single, hand selected nuclear marker that is thresholded to identify cell centers. If available a single membrane marker is used to expand the cell centers to full cells masks, often using watershed type algorithms. -
Conference Program
!"#$%&'(&)'*+%*+,! ! #$%&'%('! "! )*+,%-*!.%!/0112345""6! #! )*+,%-*!.%!789+:&;!<(*+=&>6! $! <&'.(8,.:%&'!?%(!#*'':%&!1@=:('!=&>!A(*'*&.*('! %! B(=,C!D:'.! &! 7=:+E!#,@*>8+*!=.!=!/+=&,*! '! A=$*(!A(*'*&.=.:%&'!9E!B(=,C! ("! GeneticGenetic andand EvolutionaryEvolutionary1%&?*(*&,*!F+%%($+=& Computation Computation! (%!! Conference Conference 2009 2012 ConferenceConference2(G=&:H*(' ProgramProgram! ('! Table of Contents B(=,C!1@=:('! ()! GECCO has grown up Table of Contents 1 A(%G(=-!1%--:..**! "*! Program Outline 2 WelcomeOrganizers from General Chair! I*'.!A=$*(!J%-:&**'! "'! 3 4 Instruction for Session Chairs and Presenters! 4 Program Committee K*E&%.*'! #*! 5 ProgramPapers Schedule Nominated and Floor for BestPlans Paper! Awards 5 12 B@*!L.@!M&&8=+!NO8-:*'P!MQ=(>'! #"! TrackAbout List the and Evolutionary Abbreviations! Computation in Practice Track 5 15 Floor Plans! 6 Tutorials and Workshops0R%+8.:%&=(E!1%-$8.=.:%&!:&!A(=,.:,* Schedule ! ##! 16 Daily Schedule at a Glance! 8 Meeting Rooms Floor Plan 18 GECCO-2012 Conference B@*!/0112!<&>8'.(:=+!1@=++*&G*Organizers and Track Chairs! ! #$! 13 Wednesday Workshop Presentations 20 GECCO-2012Thursday Program Workshop Committee 45""!/0112!1%-$*.:.:%&'Presentations Members ! ! #%! 15 30 Best PaperPaper NominationsPresentations! Sessions-at-a-Glance 24 34 )%(C'@%$!A(*'*&.=.:%&'! #'! Keynote withThursday, Chris Adami 18:30! Poster Session 30 36 Keynote forInstructions the GDS track for Sessionwith Stuart Chairs+,-./!+/.0.1 Kauffman and Presenters! 2,23410 ! ! 30 55 Friday, 8:30 Presentations -
Protocol of Image Analysis
Protocol of Image Analysis - Step-by-step instructional guide using the software Fiji, Ilastik and Drishti by Stella Gribbe 1. Installation The open-source software Fiji, Ilastik and Drishti are needed in order to perform image analysis as described in this protocol. Install the software for each program on your system, using the html- addresses below. In your web browser, open the Fiji homepage for downloads: https://fiji.sc/#download. Select the software option suited for your operating system and download it. In your web browser, open the Ilastik homepage for 'Download': https://www.ilastik.org/download.html. For this work, Ilastik version 1.3.2 was installed, as it was the most recent version. Select the Download option suited for your operating system and follow the download instructions on the webpage. In your web browser, open the Drishti page on Github: https://github.com/nci/drishti/releases. Select the versions for your operating system and download 'Drishti version 2.6.3', 'Drishti version 2.6.4' and 'Drishti version 2.6.5'. 1 2. Pre-processing Firstly, reduce the size of the CR.2-files from your camera, if the image files are exceedingly large. There is a trade-off between image resolution and both computation time and feasibility. Identify your necessary minimum image resolution and compress your image files to the largest extent possible, for instance by converting them to JPG-files. Large image data may cause the software to crash, if your internal memory capacity is too small. Secondly, convert your image files to inverted 8-bit grayscale JPG-files and adjust the parameter brightness and contrast to enhance the distinguishability of structures. -
Automated Segmentation and Single Cell Analysis for Bacterial Cells
Automated Segmentation and Single Cell Analysis for Bacterial Cells -Description of the workflow and a step-by-step protocol- Authors: Michael Weigert and Rolf Kümmerli 1 General Information This document presents a new workflow for automated segmentation of bacterial cells, andthe subsequent analysis of single-cell features (e.g. cell size, relative fluorescence values). The described methods require the use of three freely available open source software packages. These are: 1. ilastik: http://ilastik.org/ [1] 2. Fiji: https://fiji.sc/ [2] 3. R: https://www.r-project.org/ [4] Ilastik is an interactive, machine learning based, supervised object classification and segmentation toolkit, which we use to automatically segment bacterial cells from microscopy images. Segmenta- tion is the process of dividing an image into objects and background, a bottleneck in many of the current approaches for single cell image analysis. The advantage of ilastik is that the segmentation process does not require priors (e.g. information oncell shape), and can thus be used to analyze any type of objects. Furthermore, ilastik also works for low-resolution images. Ilastik involves a user supervised training process, during which the software is trained to reliably recognize the objects of interest. This process creates a ”Object Prediction Map” that is used to identify objects inimages that are not part of the training process. The training process is computationally intensive. We thus recommend to use of a computer with a multi-core CPU (ilastik supports hyper threading) and at least 16GB of RAM-memory. Alternatively, a computing center or cloud computing service could be used to speed up the training process. -
Titel Untertitel
KNIME Image Processing Nycomed Chair for Bioinformatics and Information Mining Department of Computer and Information Science Konstanz University, Germany Why Image Processing with KNIME? KNIME UGM 2013 2 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … = Single, individual, case specific, incompatible solutions KNIME UGM 2013 3 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … → Integration! KNIME UGM 2013 4 KNIME as integration platform KNIME UGM 2013 5 Integration: What and How? KNIME UGM 2013 6 Integration ImgLib2 • Developed at MPI-CBG Dresden • Generic framework for data (image) processing algoritms and data-structures • Generic design of algorithms for n-dimensional images and labelings • http://fiji.sc/wiki/index.php/ImgLib2 → KNIME: used as image representation (within the data cells); basis for algorithms KNIME UGM 2013 7 Integration ImageJ/Fiji • Popular, highly interactive image processing tool • Huge base of available plugins • Fiji: Extension of ImageJ1 with plugin-update mechanism and plugins • http://rsb.info.nih.gov/ij/ & http://fiji.sc/ → KNIME: ImageJ Macro Node KNIME UGM 2013 8 Integration ImageJ2 • Next-generation version of ImageJ • Complete re-design of ImageJ while maintaining backwards compatibility • Based on ImgLib2 -
Metadefender Core V4.17.3
MetaDefender Core v4.17.3 © 2020 OPSWAT, Inc. All rights reserved. OPSWAT®, MetadefenderTM and the OPSWAT logo are trademarks of OPSWAT, Inc. All other trademarks, trade names, service marks, service names, and images mentioned and/or used herein belong to their respective owners. Table of Contents About This Guide 13 Key Features of MetaDefender Core 14 1. Quick Start with MetaDefender Core 15 1.1. Installation 15 Operating system invariant initial steps 15 Basic setup 16 1.1.1. Configuration wizard 16 1.2. License Activation 21 1.3. Process Files with MetaDefender Core 21 2. Installing or Upgrading MetaDefender Core 22 2.1. Recommended System Configuration 22 Microsoft Windows Deployments 22 Unix Based Deployments 24 Data Retention 26 Custom Engines 27 Browser Requirements for the Metadefender Core Management Console 27 2.2. Installing MetaDefender 27 Installation 27 Installation notes 27 2.2.1. Installing Metadefender Core using command line 28 2.2.2. Installing Metadefender Core using the Install Wizard 31 2.3. Upgrading MetaDefender Core 31 Upgrading from MetaDefender Core 3.x 31 Upgrading from MetaDefender Core 4.x 31 2.4. MetaDefender Core Licensing 32 2.4.1. Activating Metadefender Licenses 32 2.4.2. Checking Your Metadefender Core License 37 2.5. Performance and Load Estimation 38 What to know before reading the results: Some factors that affect performance 38 How test results are calculated 39 Test Reports 39 Performance Report - Multi-Scanning On Linux 39 Performance Report - Multi-Scanning On Windows 43 2.6. Special installation options 46 Use RAMDISK for the tempdirectory 46 3. -
Krawtchouk Matrices from Classical and Quantum Random Walks
Krawtchouk matrices from classical and quantum random walks Philip Feinsilver and Jerzy Kocik Abstract. Krawtchouk’s polynomials occur classically as orthogonal polyno- mials with respect to the binomial distribution. They may be also expressed in the form of matrices, that emerge as arrays of the values that the polynomials take. The algebraic properties of these matrices provide a very interesting and accessible example in the approach to probability theory known as quantum probability. First it is noted how the Krawtchouk matrices are connected to the classical symmetric Bernoulli random walk. And we show how to derive Krawtchouk matrices in the quantum probability context via tensor powers of the elementary Hadamard matrix. Then connections with the classical situa- tion are shown by calculating expectation values in the quantum case. 1. Introduction Some very basic algebraic rules can be expressed using matrices. Take, for example, (a + b)2 = a2 + 2ab + b2 1 1 1 (2) (a + b)(a − b) = a2 − b2 ⇒ Φ = 2 0 −2 (a − b)2 = a2 − 2ab + b2 1 −1 1 (the expansion coefficients make up the columns of the matrix). In general, we make the definition: Definition 1.1. The N th-order Krawtchouk matrix Φ(N) is an (N +1)×(N +1) matrix, the entries of which are determined by the expansion: N N−j j X i (N) (1.1) (1 + v) (1 − v) = v Φij . i=0 The left-hand-side G(v) = (1 + v)N−j (1 − v)j is thus the generating function for the row entries of the jth column of Φ(N). -
A Computational Framework to Study Sub-Cellular RNA Localization
ARTICLE DOI: 10.1038/s41467-018-06868-w OPEN A computational framework to study sub-cellular RNA localization Aubin Samacoits1,2, Racha Chouaib3,4, Adham Safieddine3,4, Abdel-Meneem Traboulsi3,4, Wei Ouyang 1,2, Christophe Zimmer 1,2, Marion Peter3,4, Edouard Bertrand3,4, Thomas Walter 5,6,7 & Florian Mueller 1,2 RNA localization is a crucial process for cellular function and can be quantitatively studied by single molecule FISH (smFISH). Here, we present an integrated analysis framework to ana- 1234567890():,; lyze sub-cellular RNA localization. Using simulated images, we design and validate a set of features describing different RNA localization patterns including polarized distribution, accumulation in cell extensions or foci, at the cell membrane or nuclear envelope. These features are largely invariant to RNA levels, work in multiple cell lines, and can measure localization strength in perturbation experiments. Most importantly, they allow classification by supervised and unsupervised learning at unprecedented accuracy. We successfully vali- date our approach on representative experimental data. This analysis reveals a surprisingly high degree of localization heterogeneity at the single cell level, indicating a dynamic and plastic nature of RNA localization. 1 Unité Imagerie et Modélisation, Institut Pasteur and CNRS UMR 3691, 28 rue du Docteur Roux, 75015 Paris, France. 2 C3BI, USR 3756 IP CNRS, 28 rue du Docteur Roux, 75015 Paris, France. 3 Institut de Génétique Moléculaire de Montpellier, University of Montpellier, CNRS, Montpellier, France. 4 Equipe labellisée Ligue Nationale Contre le Cancer, Paris, France. 5 MINES ParisTech, PSL-Research University, CBIO-Centre for Computational Biology, 75006 Paris, France. 6 Institut Curie, PSL Research University, 75005 Paris, France.