List of Useful Computational Software
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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. -
Modern Programming Languages CS508 Virtual University of Pakistan
Modern Programming Languages (CS508) VU Modern Programming Languages CS508 Virtual University of Pakistan Leaders in Education Technology 1 © Copyright Virtual University of Pakistan Modern Programming Languages (CS508) VU TABLE of CONTENTS Course Objectives...........................................................................................................................4 Introduction and Historical Background (Lecture 1-8)..............................................................5 Language Evaluation Criterion.....................................................................................................6 Language Evaluation Criterion...................................................................................................15 An Introduction to SNOBOL (Lecture 9-12).............................................................................32 Ada Programming Language: An Introduction (Lecture 13-17).............................................45 LISP Programming Language: An Introduction (Lecture 18-21)...........................................63 PROLOG - Programming in Logic (Lecture 22-26) .................................................................77 Java Programming Language (Lecture 27-30)..........................................................................92 C# Programming Language (Lecture 31-34) ...........................................................................111 PHP – Personal Home Page PHP: Hypertext Preprocessor (Lecture 35-37)........................129 Modern Programming Languages-JavaScript -
TANGO Device
School on TANGO Controls system Basics of TANGO Lorenzo Pivetta Claudio Scafuri Graziano Scalamera http://www.tango-controls.org L.Pivetta, C.Scafuri, G.Scalamera School on TANGO Control System - Trieste 4-8th July 2016 2 Prerequisites To better understand the training a background on the following arguments is desirable: ● Programming language ● Object oriented programming ● Linux/UNIX operating system ● Networking ● Control systems L.Pivetta, C.Scafuri, G.Scalamera School on TANGO Control System - Trieste 4-8th July 2016 3 Outline 1 - What is TANGO? 2 - TANGO architecture Language/OS/Compilers Device hierarchy CORBA and ZeroMQ TANGO domains TANGO device and device server TANGO Database Communication models 3 - TANGO configuration/tools Multicast Jive Polling Starter/Astor Events Pogo Alarms TANGO installation Groups Client basics TANGO ACL Logging system Historical DataBase 4 – Examples Test device L.Pivetta, C.Scafuri, G.Scalamera School on TANGO Control System - Trieste 4-8th July 2016 4 What is TANGO? Scientific workspaces Native client applications In short: Industrial SCADA Control system framework TANGO Based on CORBA and ZMQ C++ TANGO Java Archiving Python System Centralized config. database TANGO TANGO TANGO TANGO Clients binding binding binding binding (CLI/GUI) Software bus for distributed TANGO software bus objects Provides unified interface to Device Device Device Device Device Device Device Server Server Server Server Server Server Server all equipments hiding how they are HV ps + Pylon OPC UA Data Motion TANGO SNMP connected/managed -
Qualitative Comparison of Conventional and Oblique MRI for Detection of Herniated Spinal Discs
Qualitative Comparison of Conventional and Oblique MRI for Detection of Herniated Spinal Discs Doug Dean Final Project Presentation ENGN 2500: Medical Image Analysis May 16, 2011 Tuesday, May 17, 2011 Outline • Review of the problem presented in the paper: “A comparison of angled sagittal MRI and conventional MRI in the diagnosis of herniated disc and stenosis in the cervical foramen” (Authors: Shim JH, Park CK, Lee JH, et. al) • Approach to solve this problem • Data Acquisition • Analysis Methods • Results • Discussion/Conclusions Tuesday, May 17, 2011 Review of Problem • Difficult to identify herniated discs and spinal stenosis using conventional (2D) MRI techniques • These conventional methods result in patients condition being misdiagnosed. “Conventional MRI”: Images acquired along one of three anatomical planes Tuesday, May 17, 2011 3D reconstructive CT Axial, T2-weighted Image: Image shows that the Cervical Foramen is cervical foramina are directed at 45 degrees directed downward around with respect to coronal 10-15 degrees with plane. respect to axial plane Tuesday, May 17, 2011 Orientation of Images Conventional MRI: Sagittal Protocol Oblique MRI: Sagittal Protocol Tuesday, May 17, 2011 Timeline • Week 1 (4/11-4/16) • Work on developing MR imaging protocols and sequences • Recruit volunteers (~4-5 volunteers) • Week 2 (4/17-4/23) • Continue developing imaging sequences and begin data acquisition at the MRI facility • Assisted by Dr. Deoni • Week 3&4 (4/24-5/7) • Continue developing and testing sequence • 4/27/2011: Acquisition of first subject • Mid Project Presentation: Describe the imaging protocols, present data that had been acquired from previous week, describe what still needs to be done. -
A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research Krzysztof J
bioRxiv preprint first posted online Feb. 12, 2016; doi: http://dx.doi.org/10.1101/039354. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY 4.0 International license. A practical guide for improving transparency and reproducibility in neuroimaging research Krzysztof J. Gorgolewski and Russell A. Poldrack Department of Psychology, Stanford University Abstract Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility. Introduction The question of how the brain creates the mind has captivated humankind for thousands of years. With recent advances in human in vivo brain imaging, we how have effective tools to peek into biological underpinnings of mind and behavior. Even though we are no longer constrained just to philosophical thought experiments and behavioral observations (which undoubtedly are extremely useful), the question at hand has not gotten any easier. These powerful new tools have largely demonstrated just how complex the biological bases of behavior actually are. -
MITK-Modelfit: a Generic Open-Source Framework for Model Fits and Their Exploration in Medical Imaging – Design, Implementatio
MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging – design, implementation and application on the example of DCE-MRI Charlotte Debus1-5,*,#, Ralf Floca5,6,*,#, Michael Ingrisch7, Ina Kompan5,6, Klaus Maier-Hein5,6,8, Amir Abdollahi1-5, and Marco Nolden6 1German Cancer Consortium (DKTK), Heidelberg, Germany 2Translational Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany 3Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany 4National Center for Tumor Diseases (NCT), Heidelberg, Germany 5Heidelberg Institute of Radiation Oncology (HIRO), Germany 6Division of Medical Image Computing, German Cancer Research Center DKFZ, Germany 7Department of Radiology, University Hospital Munich, Ludwig-Maximilians-University Munich, Germany 8Section Pattern Recognition, Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany # Correspondence: Charlotte Debus, PhD Department of Translational Radiation Oncology Heidelberg Ion-Beam Therapy Center (HIT) Im Neuenheimer Feld 450 69120 Heidelberg, Germany Email: [email protected] Phone: +49 6221 6538281 Ralf Floca, PhD Division of Medical Image Computing German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 69120 Heidelberg, Germany Email: [email protected] Phone: + 49 6221 42 2560 * Shared first-authors 1 Abstract Background: Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in dynamic contrast- enhanced (DCE) magnetic resonance imaging (MRI)/computed tomography (CT), apparent diffusion coefficient calculations and intravoxel incoherent motion modeling in diffusion-weighted MRI and Z- spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. -
Anatomical Models from Imaging Data
Prof. Steven S. Saliterman Department of Biomedical Engineering, University of Minnesota http://saliterman.umn.edu/ Magnetic Resonance Imaging (MRI) ◦ Human max. is 3T (Tesla) – resolution of 250µm x 250µm 0.5mm. ◦ High spatial resolution µMRI, 7-10T, 5-200µm. ◦ Magnetic nanoparticles. Computed tomography (CT)– Computer Axial Tomography ◦ Typical resolution of 0.24 – 0.3mm. ◦ µCT, resolution of 1-200µm. Ultrasound ◦ Resolution of 1mm x 1.mm x 0.2mm. PET – Positron emission tomography SPECT – Single photon emission computed tomography Optical Coherence Tomography (OCT) Traditional optical techniques. Prof. Steven S. Saliterman Prof. Steven S. Saliterman Mayo Foundation for Medical Education and Research Prof. Steven S. Saliterman CT scan/PET Scan/ Combined Mayo Foundation for Medical Education and Research Prof. Steven S. Saliterman Purpose ◦ To delineate and isolate anatomical features within an imaging database- e.g. bone, cartilage, soft tissue, edema; muscle, lung, brain & other organs, and tumors. Method ◦ Extract images from DICOM files (ITK-Snap, Onis) and possible deindentifying them for HIPPA regulations (DICOMCleaner). ◦ Segmentation Software (ITK-Snap, Materialise Mimics, Materialise 3- matic). Pre-segmentation Phase - identify parts of image as foreground and background. Active Contour Phase - manual and semiautomatic methods. ◦ Editing and fixing mesh files (.STL) - Autodesk Meshmixer. ◦ Slicer software – Simplify3D and Repetier. G-coding for the specific bioprinter - e.g. Slic3R (printer customized interface to control what happens in a sequence of control steps.) Prof. Steven S. Saliterman Sagittal or Median Parasagittal (Yellow) Transverse or Axial Frontal or Coronal Prof. Steven S. Saliterman Image, Wikipedia Manual Segmentation… Prof. Steven S. Saliterman Prof. Steven S. Saliterman Prof. Steven S. Saliterman Prof. -
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. -
Survey of Databases Used in Image Processing and Their Applications
International Journal of Scientific & Engineering Research Volume 2, Issue 10, Oct-2011 1 ISSN 2229-5518 Survey of Databases Used in Image Processing and Their Applications Shubhpreet Kaur, Gagandeep Jindal Abstract- This paper gives review of Medical image database (MIDB) systems which have been developed in the past few years for research for medical fraternity and students. In this paper, I have surveyed all available medical image databases relevant for research and their use. Keywords: Image database, Medical Image Database System. —————————— —————————— 1. INTRODUCTION Measurement and recording techniques, such as electroencephalography, magnetoencephalography Medical imaging is the technique and process used to (MEG), Electrocardiography (EKG) and others, can create images of the human for clinical purposes be seen as forms of medical imaging. Image Analysis (medical procedures seeking to reveal, diagnose or is done to ensure database consistency and reliable examine disease) or medical science. As a discipline, image processing. it is part of biological imaging and incorporates radiology, nuclear medicine, investigative Open source software for medical image analysis radiological sciences, endoscopy, (medical) Several open source software packages are available thermography, medical photography and for performing analysis of medical images: microscopy. ImageJ 3D Slicer ITK Shubhpreet Kaur is currently pursuing masters degree OsiriX program in Computer Science and engineering in GemIdent Chandigarh Engineering College, Mohali, India. E-mail: MicroDicom [email protected] FreeSurfer Gagandeep Jindal is currently assistant processor in 1.1 Images used in Medical Research department Computer Science and Engineering in Here is the description of various modalities that are Chandigarh Engineering College, Mohali, India. E-mail: used for the purpose of research by medical and [email protected] engineering students as well as doctors. -
Sistema De Detección Y Ubicación De Marcadores Visuales Para El Rastreo De Robots Móviles
1 UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO FACULTAD DE INGENIERÍA SISTEMA DE DETECCIÓN Y UBICACIÓN DE MARCADORES VISUALES PARA EL RASTREO DE ROBOTS MÓVILES T E S I S Que para obtener el título de INGENIERO MECATRÓNICO P R E S E N T A EMMANUEL TAPIA BRITO DIRECTOR DE TESIS: DR. VÍCTOR JAVIER GONZÁLEZ VILLELA MÉXICO D.F. JULIO 2013 Dedicatoria A mis padres, por su incondicional apoyo, por ser fuente de inspiración, por su esfuerzo derramado día a día y por aguantarme durante todos estos años. * A Getsemaní por llenar de inmenso amor y alegría mi corazón y por haber compartido conmigo una gran etapa de mi vida. * A Sofía por brindarme tu apoyo y el amor que me dio ánimos para alcanzar esta meta. * A todos mis amigos por haber formado parte de mi vida universitaria. 3 4 Agradecimientos A la Universidad Nacional Autónoma de México por la excelente educación recibida. * A los profesores de la Facultad de Ingeniería, en especial a los del Departamento de Ingeniería Mecatrónica, a quienes debo mi formación profesional. * Al grupo de trabajo MRG, por su soporte técnico y ayuda en la realización de esta Tesis. * Al Dr. Víctor Javier González Villela por la oportunidad, motivación y confianza brindada durante la realización de este proyecto. * A Oscar X. Hurtado Reynoso, por su gran ayuda en la etapa de pruebas con el robot. * A la DGAPA por el apoyo brindado a través del proyecto PAPIIT 1N115811 con título “Investigación y desarrollo en sistemas mecatrónicos: robótica móvil, robótica paralela, robótica híbrida y teleoperación”. 5 6 Resumen Esta tesis presenta los puntos principales del desarrollo de un sistema de visión por computadora con el objetivo de emplearlo como medio de asistencia para el con- trol de robots móviles. -
Medical Image Processing Software
Wohlers Report 2018 Medical Image Processing Software Medical image Patient-specific medical devices and anatomical models are almost always produced using radiological imaging data. Medical image processing processing software is used to translate between radiology file formats and various software AM file formats. Theoretically, any volumetric radiological imaging dataset by Andy Christensen could be used to create these devices and models. However, without high- and Nicole Wake quality medical image data, the output from AM can be less than ideal. In this field, the old adage of “garbage in, garbage out” definitely applies. Due to the relative ease of image post-processing, computed tomography (CT) is the usual method for imaging bone structures and contrast- enhanced vasculature. In the dental field and for oral- and maxillofacial surgery, in-office cone-beam computed tomography (CBCT) has become popular. Another popular imaging technique that can be used to create anatomical models is magnetic resonance imaging (MRI). MRI is less useful for bone imaging, but its excellent soft tissue contrast makes it useful for soft tissue structures, solid organs, and cancerous lesions. Computed tomography: CT uses many X-ray projections through a subject to computationally reconstruct a cross-sectional image. As with traditional 2D X-ray imaging, a narrow X-ray beam is directed to pass through the subject and project onto an opposing detector. To create a cross-sectional image, the X-ray source and detector rotate around a stationary subject and acquire images at a number of angles. An image of the cross-section is then computed from these projections in a post-processing step. -
Avizo Software for Industrial Inspection
Avizo Software for Industrial Inspection Digital inspection and materials analysis Digital workflow Thermo Scientific™ Avizo™ Software provides a comprehensive set of tools addressing the whole research-to-production cycle: from materials research in off-line labs to automated quality control in production environments. 3D image data acquisition Whatever the part or material you need to inspect, using © RX Solutions X-ray CT, radiography, or microscopy, Avizo Software is the solution of choice for materials characterization and defect Image processing detection in a wide range of areas (additive manufacturing, aerospace, automotive, casting, electronics, food, manufacturing) and for many types of materials (fibrous, porous, metals and alloys, ceramics, composites and polymers). Avizo Software also provides dimensional metrology with Visual inspection Dimensional metrology Material characterization advanced measurements; an extensive set of programmable & defect analysis automated analysis workflows (recipes); reporting and traceability; actual/nominal comparison by integrating CAD models; and a fully automated in-line inspection framework. With Avizo Software, reduce your design cycle, inspection times, and meet higher-level quality standards at a lower cost. + Creation and automation of inspection and analysis workflows + Full in-line integration Reporting & traceability On the cover: Porosity analysis and dimensional metrology on compressor housing. Data courtesy of CyXplus 2 3 Avizo Software for Industrial Inspection Learn more at thermofisher.com/amira-avizo Integrating expertise acquired over more than 10 years and developed in collaboration with major industrial partners in the aerospace, Porosity analysis automotive, and consumer goods industries, Avizo Software allows Imaging techniques such as CT, FIB-SEM, SEM, and TEM, allow detection of structural defects in the to visualize, analyze, measure and inspect parts and materials.