Bioimage Analysis Tools
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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. -
References Cited Avizo—Three-Dimensional Visualization
Geoinformatics 2008—Data to Knowledge 13 (for example, connect the roof of a geological layer with trial data. Wherever three-dimensional (3D) datasets need to those of an included cavity). be processed in material sciences, geosciences, or engineering applications, Avizo offers abundant state-of-the-art features 2. If a lattice unit intersects another lattice unit, we assume within an intuitive workflow and an easy-to-use graphical user that these lattice units are adjacent. This principle is for- interface. malized with proximity spaces (Naimpally and Warrack, 1970). This mathematical theory constitutes, in topology, an axiomatization of notions of “nearness”). Avizo XGreen Package—3D Visualization With the help of lattice units, two graphs are computed. Framework for Climatology Data The first one is an incidence graph, which describes relation- In these times, when climatology data have become more ships between objects and makes it possible to establish the and more complex with respect to size, resolution, and the interrelationship between them. The second one, a temporal numbers of chronological increments, the German Climate graph, represents, for one object, the evolution of its relation- Computing Center in Hamburg has chosen Mercury Com- ship with its own environment. puter Systems to develop a software extension called XGreen, This model has been used and validated in different appli- which is based on their visualization framework Avizo (for- cations, such as three-dimensional building simplification (Pou- merly known as “amira”). peau and Ruas, 2007), and in a context of a coal basin affected XGreen provides domain-specific enhancements for by anthropic subsidence due to extraction (Gueguen, 2007). -
Other Departments and Institutes Courses 1
Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses. -
Open Inventor Toolkit ACADEMIC PROGRAM FAQ Updated June 2018
Open Inventor Toolkit ACADEMIC PROGRAM FAQ Updated June 2018 • How can my organization qualify for this program ? • What support will my organization get under this program ? • How can I make an application available to collaborators • Can I use Open Inventor Toolkit in an open source project ? • What can I do with Open Inventor Toolkit under this program ? • What are some applications of Open Inventor Toolkit ? • What's the value of using Open Inventor Toolkit ? • What is Thermo Fisher expecting from my organization in exchange for the ability to use Open Inventor Toolkit free of charge ? • How does Open Inventor Toolkit relate to visualization applications ? • Can I use Open Inventor Toolkit to extend visualization applications ? • What languages, platforms and compilers are supported by Open Inventor Toolkit ? • What are some key features of Open Inventor Toolkit ? How can my organization qualify for this program ? Qualified academic and non-commercial organizations can apply for the Open Inventor Toolkit Academic Program. Through this program, individuals in your organization can be granted Open Inventor Toolkit development and runtime licenses, at no charge, for non-commercial use. Licenses provided under the Open Inventor Toolkit Academic Program allow you to both create and distribute software applications based on Open Inventor Toolkit. However, your application must be distributed free-of-charge for educational or research purposes. Your application must not generate commercial revenue nor be deployed by a corporation for in-house use nor be used in any other commercial manner Contact your account manager and we will provide you with more information on the details to qualify for this program. -
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 -
Quantification of the Pore Size Distribution of Soils: Assessment of Existing Software Using Tomographic and Synthetic 3D Images
Quantification of the pore size distribution of soils: assessment of existing software using tomographic and synthetic 3D images A.N. Houston, W. Otten, R. Falconer, O. Monga, P.C. Baveye and S.M. Hapca This is the accepted manuscript © 2017, Elsevier Licensed under the Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International: http://creativecommons.org/licenses/by-nc-nd/4.0/ The published article is available from doi: https:// doi.org/10.1016/j.geoderma.2017.03.025 1 2 3 Quantification of the pore size distribution of soils: Assessment of existing 4 software using tomographic and synthetic 3D images. 5 6 7 A. N. Houstona, W. Ottena,b, R. Falconera,c, O. Mongad, P.C.Baveyee, S.M. Hapcaa,f* 8 9 a School of Science, Engineering and Technology, Abertay University, 40 Bell Street, 10 Dundee DD1 1HG, UK. 11 b current address: School of Water, Energy and Environment, Cranfield University, 12 Cranfield MK43 0AL, UK. 13 c current address: School of Arts, Media and Computer Games , Abertay University, 14 40 Bell Street, Dundee DD1 1HG, UK. 15 d UMMISCO-Cameroun, UMI 209 UMMISCO, University of Yaoundé, IRD, University 16 of Paris 6, F-93143 Bondy Cedex, France. 17 e UMR ECOSYS, AgroParisTech, Université Paris-Saclay, Avenue Lucien 18 Brétignières, Thiverval-Grignon 78850, France. 19 f current address: Dundee Epidemiology and Biostatistics Unit, School of Medicine, 20 Dundee University, Kirsty Semple Way, Dundee DD2 4BF, UK. 21 22 * Corresponding author. E-mail: [email protected] 1 23 Abstract: 24 25 The pore size distribution (PSD) of the void space is widely used to predict a range of 26 processes in soils. -
Downloaded from the Cellprofiler Site [31] to Provide a Starting Point for New Analyses
Open Access Software2006CarpenteretVolume al. 7, Issue 10, Article R100 CellProfiler: image analysis software for identifying and quantifying comment cell phenotypes Anne E Carpenter*, Thouis R Jones*†, Michael R Lamprecht*, Colin Clarke*†, In Han Kang†, Ola Friman‡, David A Guertin*, Joo Han Chang*, Robert A Lindquist*, Jason Moffat*, Polina Golland† and David M Sabatini*§ reviews Addresses: *Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA. †Computer Sciences and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. ‡Department of Radiology, Brigham and Women's Hospital, Boston, MA 02115, USA. §Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. Correspondence: David M Sabatini. Email: [email protected] Published: 31 October 2006 Received: 15 September 2006 Accepted: 31 October 2006 reports Genome Biology 2006, 7:R100 (doi:10.1186/gb-2006-7-10-r100) The electronic version of this article is the complete one and can be found online at http://genomebiology.com/2006/7/10/R100 © 2006 Carpenter et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. deposited research Cell<p>CellProfiler, image analysis the software first free, open-source system for flexible and high-throughput cell image analysis is described.</p> Abstract Biologists can now prepare and image thousands of samples per day using automation, enabling chemical screens and functional genomics (for example, using RNA interference). Here we describe the first free, open-source system designed for flexible, high-throughput cell image analysis, research refereed CellProfiler. -
Cellprofiler: Image Analysis Software for Identifying and Quantifying Cell Phenotypes
CellProfiler: image analysis software for identifying and quantifying cell phenotypes The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Genome Biology. 2006 Oct 31;7(10):R100 As Published http://dx.doi.org/10.1186/gb-2006-7-10-r100 Publisher BioMed Central Ltd Version Final published version Citable link http://hdl.handle.net/1721.1/58762 Terms of Use Creative Commons Attribution Detailed Terms http://creativecommons.org/licenses/by/2.0 1 2 3 Table of Contents Getting Started: MaskImage . 96 Introduction . 6 Morph . 110 Installation . 7 OverlayOutlines . 111 Getting Started with CellProfiler . 9 PlaceAdjacent . 112 RescaleIntensity . 115 Resize . 116 Help: Rotate . 118 BatchProcessing . 11 Smooth . 122 Colormaps. .16 Subtract . 125 DefaultImageFolder . 17 SubtractBackground . 126 DefaultOutputFolder . 18 Tile.............................................127 DeveloperInfo . 19 FastMode . 28 MatlabCrash . 29 Object Processing modules: OutputFilename . 30 ClassifyObjects . 41 PixelSize . 31 ClassifyObjectsByTwoMeasurements . 42 Preferences. .32 ConvertToImage . 45 SkipErrors . 33 Exclude . 65 TechDiagnosis . 34 ExpandOrShrink . 66 FilterByObjectMeasurement . 71 IdentifyObjectsInGrid . 76 File Processing modules: IdentifyPrimAutomatic . 77 CreateBatchFiles . 51 IdentifyPrimManual . 83 ExportToDatabase . 68 IdentifySecondary . 84 ExportToExcel. .70 IdentifyTertiarySubregion. .88 LoadImages . 90 Relate . 113 LoadSingleImage . 94 LoadText. .95 RenameOrRenumberFiles . -
Avizo® Earth the 3D Visualization Software for Geosciences and Oil & Gas
DataSheet Avizo® Earth The 3D Visualization Software for Geosciences and Oil & Gas • Manages complex multi-modality information • Increases understanding of complex datasets • Simplifies critical decision-making • Scalable visualization application framework • Extensible architecture Avizo® Earth Edition software is a powerful, multifaceted On-Demand Innovation framework for integrating, manipulating, and visualizing your State-of-the-art visualization techniques provide the foundation seismic, geology, reservoir engineering, and petrography for Avizo, simplifying the complex to improve your efficiency, datasets. Geophysicists and geologists can use this solution so that you can meet your toughest business challenges. The to import, manage, interact with, and visualize multiple extensible architecture makes it easy for you to develop new sources within a single environment. modules, file formats, or types of visualization, and to integrate your own unique computational modules. Avizo’s high-quality The Avizo tool is applicable across the full spectrum of visual volume rendering, even for extremely large datasets and applications, including seismic data QC, pre-processing, and intensive GPU use, includes advanced lighting and iso-surface interpretation; reservoir modeling, characterization, and rendering, data transforming, and data combining on the fly. simulation; drilling planning, flow simulation, and engineering; 3D petrography, core sample analysis, and borehole imaging; and Cost-Effective Collaboration engineering design, training, and simulation. Using Avizo, you can import your data from multiple sources and integrate it all into a single environment, which frees you to focus On-Demand Information on your core competencies. The easy and flexible scripting facility Whether you are a geophysicist, a geologist, or a project manager, lets you customize Avizo and automate many tasks to increase Avizo provides you with a global solution for accessing and your effectiveness. -
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 -
9. Biomedical Imaging Informatics Daniel L
9. Biomedical Imaging Informatics Daniel L. Rubin, Hayit Greenspan, and James F. Brinkley After reading this chapter, you should know the answers to these questions: 1. What makes images a challenging type of data to be processed by computers, as opposed to non-image clinical data? 2. Why are there many different imaging modalities, and by what major two characteristics do they differ? 3. How are visual and knowledge content in images represented computationally? How are these techniques similar to representation of non-image biomedical data? 4. What sort of applications can be developed to make use of the semantic image content made accessible using the Annotation and Image Markup model? 5. Describe four different types of image processing methods. Why are such methods assembled into a pipeline when creating imaging applications? 6. Give an example of an imaging modality with high spatial resolution. Give an example of a modality that provides functional information. Why are most imaging modalities not capable of providing both? 7. What is the goal in performing segmentation in image analysis? Why is there more than one segmentation method? 8. What are two types of quantitative information in images? What are two types of semantic information in images? How might this information be used in medical applications? 9. What is the difference between image registration and image fusion? Given an example of each. 1 9.1. Introduction Imaging plays a central role in the healthcare process. Imaging is crucial not only to health care, but also to medical communication and education, as well as in research. -
Bioimage Informatics
Bioimage Informatics Lecture 22, Spring 2012 Empirical Performance Evaluation of Bioimage Informatics Algorithms Image Database; Content Based Image Retrieval Emerging Applications: Molecular Imaging Lecture 22 April 23, 2012 1 Outline • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 2 • Empirical performance evaluation of bioimage Informatics algorithm • Image database; content-based image retrieval • Introduction to molecular imaging 3 Overview of Algorithm Evaluation (I) • Two sets of data are required - Input data - Corresponding correct output (ground truth) • Sources of input data - Actual/experimental data, often from experiments - Synthetic data, often from computer simulation • Actual/experimental data - Essential for performance evaluation - May be costly to acquire - May not be representative - Ground truth often is unknown Overview of Algorithm Evaluation (II) • What exactly is ground truth? Ground truth is a term used in cartography, meteorology, analysis of aerial photographs, satellite imagery and a range of other remote sensing techniques in which data are gathered at a distance. Ground truth refers to information that is collected "on location." - http://en.wikipedia.org/wiki/Ground_truth • Synthetic (simulated) data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data 5 Overview of Algorithm Evaluation (III) • Simulated data Advantages - Ground truth is known - Usually low-cost Disadvantages - Difficult to fully represent the original data • Realistic synthetic data - To use as much information from real experimental data as possible • Quality control of manual analysis Test Protocol Development (I) • Implementation - Source codes are not always available and often are on different platforms. • Parameter setting - This is one of the most challenging issues.