Training school in the use of open source software V June 2009 Ancaster Hall, University Park
|
|
|
- Darlene Freeman
- 5 years ago
- Views:
Transcription
1 Training school in the use of open source software V June 2009 Ancaster Hall, University Park Target Audience: (1) Pathologists who are newcomers to image analysis (i.e. with little or no experience) (2) Signal processing engineers (i.e. interested in medical imaging) (3) PhD students Objectives: (a) To train Pathologists in the basic principles of image analysis of standard brightfield images (segmentation, 16/32/64 bit images, thresholding etc) (b) To provide a comprehensive list of image capture technologies (c) To provide a comprehensive list of image analysis software (d) To provide a list of on-line resources and other communities involved in imagej (e) To provide training in the use of open source image analysis software for the capture of images from a variety of sources (f) To provide training in basic image analysis using open source software (measurements / masking / ROI / segmentation / texturing / cell counting / ploidy analysis etc) By the end of the session the attendees should: (i) Understand the theory and limitations of image capture and manipulation (ii) Be able to use ImageJ to view images captured by different hardwares (iii) Be able to perform basic image analysis (iv) Identify appropriate plug-ins for their requirements Format of training school: (a) Formal lectures (b) Interactive tutorials (c) Problem solving exercises (d) Problems / presentations brought in by attendees (e) Examples of solutions
2 (f) Discussion sessions (including commercial companies) to discuss future directions Proposed location: Ancaster Hall, Nottingham University Faculty M Ilyas (organiser) Vincenzo Dela Mea Marcial Rojo Branimir Reljin Gabriel Landini Paulette Herlin Qiu Guoping Arvydas Laurinavicius Nicolas Elie
3 Preliminary program: th June 2009 All attendees to bring own laptop Day 1 09:00 09:30 Introduction the role of image analysis in Pathology (covering: image analysis as diagnostic aids / telepathology / image analysis in research etc.) Marcial Rojo 09:30 10:15 What is a digital image? (covering: pixels, formats such as jpg/bmp/tiff etc, brightfield images rgb/cmyk/cielab/binary transformation, multispectral images) Branimir Reljin 10:15 10:45 Tea / Coffee 10:45 11:30 Methods of image capture and viewing (covering: digital photography, the different slide scanners, commercial software sold with scanners, use of ImageJ to view scanned images, what the meaning of 8 or 16 bit is and why it s important). Marcial Rojo 11:30 12:15 Basic use of imagej (covering: the basic functions/tools of the program, measuring size and area, counting particles manually etc) Vincenzo Delamea 12:15-12:30 Loading ImageJ onto laptops (including plug-ins) 12:30 13:30 Lunch 13:30 14:00 Worked examples with ImageJ (Led by Vincenzo Delamea) 14:00 15:30 Practical session of easy image analysis problems (Led by Vincenzo Delamea) 15:30 16:00 Tea / coffee 16:00 17:00 Going through practical examples (Led by Vincenzo Delamea)
4 Day 2 09:00 09:45 Image fragmentation (covering: identifying and marking ROIs, applying masks to sequential images) Qiu Guoping 09:45 10:15 Worked examples with ImageJ - (Led by Paulette Herlin) 10:15 10:45 Tea / coffee 10:45 12:15 Practical session of image analysis problems (Led by Paulette Herlin) 12:15 12:45 Going through practical examples (Led by Paulette Herlin) 12:45 13:45 Lunch 13:45 14:15 Further image fragmentation (covering: segmentation, binary transformation, particle counting, thresholding) Gabriel Landini 14:15 14:45 Worked examples with ImageJ - (Led by Gabriel Landini) 14:45 15:30 Practical session of image analysis problems (Led by Gabriel Landini) 15:30 16:00 Tea / Coffee 16:00 16:30 Practical session of image analysis problems (Led by Gabriel Landini) 16:30 17:00 Going through practical examples (Led by Gabriel Landini)
5 Day 3 09:00 09:45 More fragmentation analysis ( texture analysis, k-means clustering) Gabriel Landini 09:45 10:30 Immunohistochemistry (covering: nuclear / cytoplasmic / membranous staining, segmentation and thresholding, viewing through RGB / Cielab., TMA databasing) Marcial Rojo 10:30 11:30 Practical session of image analysis problems (Led by Marcial Rojo) and Coffee 11:30 12:00 Going through practical examples (Led by Marcial Rojo) 12:00 13:30 Presentation by Slidepath (i) SlidePath image analysis algorithms, (ii) integrating Image J into slidepath, (iii) Databasing and high-throughput analysis 13:30 14:30 Lunch and finish
Color aspects and Color Standardization in Digital Microscopy
Color aspects and Color Standardization in Digital Microscopy Yukako Yagi, PhD yyagi@partners.org Director of the MGH Pathology Imaging & Communication Technology Center Assistant Professor of Pathology,
Second Announcement Call for Participation. (Evaluation Criteria added)
Second Announcement Call for Participation 2 nd International Scanner Contest (ISC) (Evaluation Criteria added) P. Hufnagl 1, T. Schrader 1, 2, M.G. Rojo 3, A. Laurinavicius 4, G. Kayser 5, Y. Yagi 6 1
ImageJ: Introduction to Image Analysis 3 May 2012 Jacqui Ross
Biomedical Imaging Research Unit School of Medical Sciences Faculty of Medical and Health Sciences The University of Auckland Private Bag 92019 Auckland 1142, NZ Ph: 373 7599 ext. 87438 http://www.fmhs.auckland.ac.nz/sms/biru/.
Technical Aspects in Digital Pathology
Technical Aspects in Digital Pathology Yukako Yagi, PhD yyagi@mgh.harvard.edu Director of the MGH Pathology Imaging & Communication Technology Center Assistant Professor of Pathology, Harvard Medical School
THEORY AND APPROACHES TO AUTOMATED IMAGE ANALYSIS IN DIGITAL PATHOLOGY
THEORY AND APPROACHES TO AUTOMATED IMAGE ANALYSIS IN DIGITAL PATHOLOGY Kyle Takayama, MS Charles River Laboratories EVERY STEP OF THE WAY EVERY STEP OF THE WAY MORPHOMETRY Measurements or counts performed
Computing for Engineers in Python
Computing for Engineers in Python Lecture 10: Signal (Image) Processing Autumn 2011-12 Some slides incorporated from Benny Chor s course 1 Lecture 9: Highlights Sorting, searching and time complexity Preprocessing
An Image Processing Approach for Screening of Malaria
An Image Processing Approach for Screening of Malaria Nagaraj R. Shet 1 and Dr.Niranjana Sampathila 2 1 M.Tech Student, Department of Biomedical Engineering, Manipal Institute of Technology, Manipal University,
inform ADVANCED IMAGE ANALYSIS SOFTWARE inform User Manual
inform ADVANCED IMAGE ANALYSIS SOFTWARE inform User Manual Notice The information in this document is subject to change without notice and should not be construed as a commitment by PerkinElmer, Inc. PerkinElmer
Digital Pathology Update
Digital Pathology Update J. Mark Tuthill, MD Division Head, Pathology Informatics Department of Pathology & Laboratory Medicine Henry Ford Hospital Detroit, MI mtuthil1@hfhs.org ASCT Webinar, January 2016
Stereotopix Research. Precision Pathology. Highthroughput. pathology. powered by newcast. Advantages of Stereotopix : RUO
Precision Pathology Highthroughput pathology Stereotopix Research powered by newcast RUO Researchers use quantitative microscopy in many ways with the goal of producing high-quality, quantitative results
Digital Pathology at Johns Hopkins Practical Research and Clinical Considerations
Digital Pathology at Johns Hopkins Practical Research and Clinical Considerations July 10, 2017 Alexander Baras, MD, PhD Assistant Professor of Pathology, Urology, and Oncology Associate Director of Pathology
MetaXpress Software: Cell Scoring Module
MetaXpress Software: Cell Scoring Module Cell Scoring Module Overview The Cell Scoring module can be used to analyze cells imaged in 2 wavelengths W1 should be a stain for all nuclei (e.g. DAPI, Hoechst,
IMAGE PROCESSING PROJECT REPORT NUCLEUS CLASIFICATION
ABSTRACT : The Main agenda of this project is to segment and analyze the a stack of image, where it contains nucleus, nucleolus and heterochromatin. Find the volume, Density, Area and circularity of the
BRAIN FRACTAL ANALYSIS USER S GUIDE
BRAIN FRACTAL ANALYSIS USER S GUIDE AUTHOR: KURT ZIMMER CONTRIBUTERS: JOSHUA GAO, ALEX POPLAWSKY, SAM DONOVAN INTRODUCTION Brain size and structure are highly variable across species. Common measures used
Digital Pathology and Image Analysis. Queen s University Department of Pathology and Molecular Medicine Shakeel Virk
Digital Pathology and Image Analysis Queen s University Department of Pathology and Molecular Medicine Shakeel Virk Outline Digital Pathology and Image Analysis capabilities at Queen s Laboratory for Molecular
Protocols. Graphical programming for Icy. a.k.a. programming, for the rest of us
Protocols Graphical programming for Icy a.k.a. programming, for the rest of us Foreword: Reproducible Research Quote: "Results aren't much if they can t be reproduced!" (your boss, your reviewers, your
Building a Business Case for Digital Pathology: The Time is Now! Drazen M. Jukic, MD, PhD
Building a Business Case for Digital Pathology: The Time is Now! Drazen M. Jukic, MD, PhD Business Case Telepathology vs. Digital Pathology Telepathology as a term was misunderstood in the past; It denotes
Arcturus XT Laser Capture Microdissection System AutoScanXT Software Module. User Manual
Arcturus XT Laser Capture Microdissection System AutoScanXT Software Module User Manual For Research Use Only. Not intended for any animal or human therapeutic or diagnostic use. Information in this document
Definiens. Tissue Studio 4.2. Tutorial 1: Composer and Nuclear Markers
Definiens Tissue Studio 4.2 Tutorial 1: Composer and Nuclear Markers Tutorial 1: Composer and Nuclear Markers Imprint and Version Copyright 2015 Definiens AG. All rights reserved. This document may be
MIRAX SCAN The new way of looking at pathology
Microscopy from Carl Zeiss MIRAX SCAN The new way of looking at pathology Greater reliability. Greater efficiency. Plus points for your diagnostics Better. More efficient. Quality as a factor for success
User Reference Manual
User Reference Manual IN SITU HYBRIDIZATION QUANTIFICATION (for research purposes only) Contents Overview... 3 System Requirements... 3 Compatible File Formats... 3 Input Parameters... 4 Output Parameters...
Department of Electrical Engineering
Department of Electrical Engineering Radar Remote Sensing Group Dr. Amit Kumar Mishra Private Bag X3, Rondebosch 7701, South Africa Room 7.07, George Menzies Building, Upper Campus Tel: +27 (0) 21 650
Introduction to Image Analysis with
Introduction to Image Analysis with PLEASE ENSURE FIJI IS INSTALLED CORRECTLY! WHAT DO WE HOPE TO ACHIEVE? Specifically, the workshop will cover the following topics: 1. Opening images with Bioformats
AUTOMATED MALARIA PARASITE DETECTION BASED ON IMAGE PROCESSING PROJECT REFERENCE NO.: 38S1511
AUTOMATED MALARIA PARASITE DETECTION BASED ON IMAGE PROCESSING PROJECT REFERENCE NO.: 38S1511 COLLEGE : BANGALORE INSTITUTE OF TECHNOLOGY, BENGALURU BRANCH : COMPUTER SCIENCE AND ENGINEERING GUIDE : DR.
White Blood Cells Identification and Counting from Microscopic Blood Image
White Blood Cells Identification and Counting from Microscopic Blood Image Lorenzo Putzu, and Cecilia Di Ruberto Abstract The counting and analysis of blood cells allows the evaluation and diagnosis of
Yagi Digital Microscope Calibration
Yagi Digital Microscope Calibration Method summary, assessment and suggestions for improvement W Craig Revie, International Color Consortium Introduction In the area of pathology, a type of digital microscope
Practical Image and Video Processing Using MATLAB
Practical Image and Video Processing Using MATLAB Chapter 1 Introduction and overview What will we learn? What is image processing? What are the main applications of image processing? What is an image?
FRAUNHOFER INSTITUTE FOR INTEGRATED CIRCUITS IIS. MANUAL PANORAMIC MICROSCOPY WITH istix
FRAUNHOFER INSTITUTE FOR INTEGRATED CIRCUITS IIS MANUAL PANORAMIC MICROSCOPY WITH istix CLINICAL DIAGNOSTICS AND MATERIAL SCIENCES IMPROVED BY DIGITAL MICROSCOPY B A C K G R O U N D Due to a high grade
Positive Pixel Count Algorithm. User s Guide
Positive Pixel Count Algorithm User s Guide Copyright 2004, 2006 2008 Aperio Technologies, Inc. Part Number/Revision: MAN 0024, Revision B Date: December 9, 2008 This document applies to software versions
Digital Imaging in Pathology
Digital Imaging in Pathology Anil V Parwani, MD., PhD Department of Pathology Pathology Informatics University of Pittsburgh Medical Center Pittsburgh, PA A Complete Digital Imaging System? Pathology is
DMETRIX S (FUTURE) PERSPECTIVES ON DIGITAL IMAGING & DIGITAL PATHOLOGY SYSTEMS
Michael R. Descour, Ph.D., DMetrix, Inc., & University of Arizona Lloyd J. LaComb, Jr., Ph.D., DMetrix, Inc. DMETRIX S (FUTURE) PERSPECTIVES ON DIGITAL IMAGING & DIGITAL PATHOLOGY SYSTEMS Outline of presentation
Cellular Bioengineering Boot Camp. Image Analysis
Cellular Bioengineering Boot Camp Image Analysis Overview of the Lab Exercises Microscopy and Cellular Imaging The purpose of this laboratory exercise is to develop an understanding of the measurements
Image Analysis Using R
Image Analysis Using R Chris Campbell LondonR - 13th July 2010 Steps to image analysis Image capture Clean image/reduce noise Extract information Analyze information Image Capture Light Photography Light
Stack or Trash? Quality assessment of virtual slides. Anatomic pathology & collaboration
11th European Congress on Telepathology 5th International Congress on Virtual Microscopy 6-9 June 2012, Venice, Italy Stack or Trash? Quality assessment of virtual slides David Ameisen Christophe Deroulers,
Calibration Slide for Histopathology task force Teleconference 20 February :00 (UK) / 10:00 (EST)
Calibration Slide for Histopathology task force Teleconference 20 February 2014 15:00 (UK) / 10:00 (EST) The meeting was called to order at 10:00 am (EST) by Craig Revie, chair of MIWG, with the following
InScape: Making Virtual Pathology a Reality
InScape: Making Virtual Pathology a Reality Sally S. Agersborg, M.D., Ph.D. Quest Diagnostics, Nichols Institute San Juan Capistrano, CA Company Overview Quest Diagnostics, Nichols Institute the world
Digital Microscopy. Past, Present, and Future. Cyrus V. Hedvat, MD, PhD
Digital Microscopy Past, Present, and Future Cyrus V. Hedvat, MD, PhD N Context. Digital viewing of histologic images is moving from presentations and publications to incorporation into the daily work
3. When you import the scanner for the first time make sure you change it from Full Auto Mode to that of Professional Mode.
PhotoShop Tutorials Scanning Photographic Film WorkFlow 1. Open PhotoShop 2. File > Import > choose scanner 3. When you import the scanner for the first time make sure you change it from Full Auto Mode
Manual. Cell Border Tracker. Jochen Seebach Institut für Anatomie und Vaskuläre Biologie, WWU Münster
Manual Cell Border Tracker Jochen Seebach Institut für Anatomie und Vaskuläre Biologie, WWU Münster 1 Cell Border Tracker 1. System Requirements The software requires Windows XP operating system or higher
Preprocessing and Segregating Offline Gujarati Handwritten Datasheet for Character Recognition
Preprocessing and Segregating Offline Gujarati Handwritten Datasheet for Character Recognition Hetal R. Thaker Atmiya Institute of Technology & science, Kalawad Road, Rajkot Gujarat, India C. K. Kumbharana,
SINCE2011 Singapore International NDT Conference & Exhibition, 3-4 November 2011
SINCE2011 Singapore International NDT Conference & Exhibition, 3-4 November 2011 Automated Defect Recognition Software for Radiographic and Magnetic Particle Inspection B. Stephen Wong 1, Xin Wang 2*,
The Unique Role of Lucis Differential Hysteresis Processing (DHP) in Digital Image Enhancement
The Unique Role of Lucis Differential Hysteresis Processing (DHP) in Digital Image Enhancement Brian Matsumoto, Ph.D. Irene L. Hale, Ph.D. Imaging Resource Consultants and Research Biologists, University
Report on interoperability of commercial digital pathology software
Report on interoperability of commercial digital pathology software Deliverable 1.1 Project acronym: BIOPOOL Grant Agreement: 296162 Version: Due date: Month 2 Submission date: 15/12/12 Dissemination level:
SPOT PathSuite Solutions
SPOT PathSuite Solutions The Perfect Fit PathStation TM Imaging system for the gross dissection in hood PathStand TM 40 imaging station for medium to large specimens PathSuite Is Easy To Use A turnkey
NEUROIMAGING DATA ANALYSIS SOFTWARE
NEUROIMAGING DATA ANALYSIS SOFTWARE Emilia Dana SELEŢCHI Abstract: Recent advanced in neuroimaging have significantly improved understanding of the brain and the mind. A variety of image analysis software
Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images
158 ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS VOL.5, NO.2 August 2007 Color Based Segmentation of Nuclear Stained Breast Cancer Cell Images Pornchai Phukpattaranont 1 and Pleumjit
Centre for Computational and Numerical Studies, Institute of Advanced Study in Science and Technology 2. Dept. of Statistics, Gauhati University
Cervix Cancer Diagnosis from Pap Smear Images Using Structure Based Segmentation and Shape Analysis 1 Lipi B. Mahanta, 2 Dilip Ch. Nath, 1 Chandan Kr. Nath 1 Centre for Computational and Numerical Studies,
Crystal Vis Final Report
Crystal Vis Final Report Figure 1 The breadboard microscope with LED flash illumination. Figure 2 First tests in the breadboard microscope with the printed flow cell using Thiamine as the sample. Figure
#P Quality Measures for Imaging-based Cellular Assays
Abstract #P1224 - Quality Measures for Imaging-based Cellular Assays Ilya Ravkin, Vitra Bioscience, Inc. Z-factor and related measures are useful in estimating assay variability in HTS caused by assay
How to introduce virtual microscopy (VM) in routine diagnostic pathology: constraints, ideas, and solutions
How to introduce virtual microscopy (VM) in routine diagnostic pathology: constraints, ideas, and solutions Klaus Kayser, Stephan Borkenfeld, Gian Kayser History Workflow Design of virtual microscopy Constraints
A New Connected-Component Labeling Algorithm
A New Connected-Component Labeling Algorithm Yuyan Chao 1, Lifeng He 2, Kenji Suzuki 3, Qian Yu 4, Wei Tang 5 1.Shannxi University of Science and Technology, China & Nagoya Sangyo University, Aichi, Japan,
W1005 Intro to CS and Programming in MATLAB Project Lecture (2)
W1005 Intro to CS and Programming in MATLAB Project Lecture (2) Fall 2014 http://www.cs.columbia.edu/~vovsha/w1005 Cataract Catcher Problem Summary WHO: 285 million visually impaired people worldwide [1]
International Journal of Advanced Research in Computer Science and Software Engineering
Volume 3, Issue 4, April 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Novel Approach
Brain Tumor Segmentation of MRI Images Using SVM Classifier Abstract: Keywords: INTRODUCTION RELATED WORK A UGC Recommended Journal
Brain Tumor Segmentation of MRI Images Using SVM Classifier Vidya Kalpavriksha 1, R. H. Goudar 1, V. T. Desai 2, VinayakaMurthy 3 1 Department of CNE, VTU Belagavi 2 Department of CSE, VSMIT, Nippani 3
Vehicle Number Plate Recognition with Bilinear Interpolation and Plotting Horizontal and Vertical Edge Processing Histogram with Sound Signals
Vehicle Number Plate Recognition with Bilinear Interpolation and Plotting Horizontal and Vertical Edge Processing Histogram with Sound Signals Aarti 1, Dr. Neetu Sharma 2 1 DEPArtment Of Computer Science
Detection of Malaria Parasite Using K-Mean Clustering
Detection of Malaria Parasite Using K-Mean Clustering Avani Patel, Zalak Dobariya Electronics and Communication Department Silver Oak College of Engineering and Technology, Ahmedabad I. INTRODUCTION Malaria
A Methodology to Analyze Objects in Digital Image using Matlab
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320 088X IMPACT FACTOR: 6.017 IJCSMC,
Basics of Quantitative Imaging and Image Processing Using ImageJ / Fiji. Dan White Nov 2008
MPI-CBG LMF / IPF Basics of Quantitative Imaging and Image Processing Using ImageJ / Fiji Dan White Nov 2008 Before you start writing... Presentations soon available at: http://tu-dresden.de/med/ifn Light
Enhanced Identification of Malarial Infected Objects using Otsu Algorithm from Thin Smear Digital Images
International Journal of Latest Research in Science and Technology Vol.1,Issue 2 :Page No159-163,July-August(2012) http://www.mnkjournals.com/ijlrst.htm ISSN (Online):2278-5299 Enhanced Identification
Dr. Bob on Colocalization or MSL Experiments In Learning Colocalization Using Image J
Dr. Bob on Colocalization or MSL Experiments In Learning Colocalization Using Image J Confocal microscopy is used to test whether two fluorescently labeled molecules are associated with one another. If
Programming and Optimization with Intel Xeon Phi Coprocessors. Colfax Developer Training One-day Boot Camp
Programming and Optimization with Intel Xeon Phi Coprocessors Colfax Developer Training One-day Boot Camp Abstract: Colfax Developer Training (CDT) is an in-depth intensive course on efficient parallel
BLOOD CELLS EXTRACTION USING COLOR BASED SEGMENTATION TECHNIQUE
Int. J. LifeSc. Bt & Pharm. Res. 2013 Nasrul Humaimi Mahmood et al., 2013 Research Paper BLOOD CELLS EXTRACTION USING COLOR BASED SEGMENTATION TECHNIQUE Nasrul Humaimi Mahmood 1,2 *, Poon Che Lim 2, Siti
FIJI/Image J for Quantification Hands on session
FIJI/Image J for Quantification Hands on session Dr Paul McMillan Biological Optical Microscopy Platform Hands on demonstrations FIJI set up Line Profile Thresholding Area of stain Cell confluence Nuclei
Image Database and Preprocessing
Chapter 3 Image Database and Preprocessing 3.1 Introduction The digital colour retinal images required for the development of automatic system for maculopathy detection are provided by the Department of
Realistic Skin Smoothing
TIP SHEET #7 Realistic Skin Smoothing I think it s fair to say when it comes to retouching techniques, the number of different ways to smooth skin is seemingly endless. From blurring techniques through
Manual: MasTracker for ImageJ
Manual: MasTracker for ImageJ Martin Storath 3. Juli 2007 1 1 Introduction The following are instructions for the tracking plug-in MasTracker for ImageJ. MasTracker was implemented by Martin Storath as
Differentiation of Malignant and Benign Masses on Mammograms Using Radial Local Ternary Pattern
Differentiation of Malignant and Benign Masses on Mammograms Using Radial Local Ternary Pattern Chisako Muramatsu 1, Min Zhang 1, Takeshi Hara 1, Tokiko Endo 2,3, and Hiroshi Fujita 1 1 Department of Intelligent
Algorithm User Guide:
Algorithm User Guide: Positive Pixel Count Use the Aperio algorithms to adjust (tune) the parameters until the quantitative results are sufficiently accurate for the purpose for which you intend to use
Malignancy Detection of Candidate for Basal Cell Carcinoma Using Image Processing and Artificial Neural Network
DLSU Engineering e-journal Vol. 1 No. 1, March 2007, pp.70-79 Malignancy Detection of Candidate for Basal Cell Carcinoma Using Image Processing and Artificial Neural Network Armida R. Bayot Louise Ann
Programming and Optimization with Intel Xeon Phi Coprocessors. Colfax Developer Training One-day Labs CDT 102
Programming and Optimization with Intel Xeon Phi Coprocessors Colfax Developer Training One-day Labs CDT 102 Abstract: Colfax Developer Training (CDT) is an in-depth intensive course on efficient parallel
VIRTUAL MICROSCOPY. Raymond Coleman
VIRTUAL MICROSCOPY Raymond Coleman The last few years have seen the increasing incorporation of virtual microscopy systems in universities and biomedical laboratories as part of the evolving and dramatic
Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood smear.
Biomedical Research 2018; 29 (18): 3464-3468 ISSN 0970-938X www.biomedres.info Computational approach for diagnosis of malaria through classification of malaria parasite from microscopic image of blood
MacroPATH The new line of Digital Imaging Systems for Grossing
MILESTONE H E L P I N G P A T I E N T S MacroPATH The new line of Digital Imaging Systems for Grossing If the dimensions of the specimen are not recorded, the key section not taken, and the proper special
Computer Graphics (CS/ECE 545) Lecture 7: Morphology (Part 2) & Regions in Binary Images (Part 1)
Computer Graphics (CS/ECE 545) Lecture 7: Morphology (Part 2) & Regions in Binary Images (Part 1) Prof Emmanuel Agu Computer Science Dept. Worcester Polytechnic Institute (WPI) Recall: Dilation Example
CSC 170 Introduction to Computers and Their Applications. Lecture #3 Digital Graphics and Video Basics. Bitmap Basics
CSC 170 Introduction to Computers and Their Applications Lecture #3 Digital Graphics and Video Basics Bitmap Basics As digital devices gained the ability to display images, two types of computer graphics
A new technique for distance measurement of between vehicles to vehicles by plate car using image processing
Available online at www.sciencedirect.com Procedia Engineering 32 (2012) 348 353 I-SEEC2011 A new technique for distance measurement of between vehicles to vehicles by plate car using image processing
Colour Profiling Using Multiple Colour Spaces
Colour Profiling Using Multiple Colour Spaces Nicola Duffy and Gerard Lacey Computer Vision and Robotics Group, Trinity College, Dublin.Ireland duffynn@cs.tcd.ie Abstract This paper presents an original
Spatial intensity distribution analysis Matlab user guide
Spatial intensity distribution analysis Matlab user guide August 2011 Guide on how to use the SpIDA graphical user interface. This little tutorial provides a step by step tutorial explaining how to get
Image processing. Case Study. 2-diemensional Image Convolution. From a hardware perspective. Often massively yparallel.
Case Study Image Processing Image processing From a hardware perspective Often massively yparallel Can be used to increase throughput Memory intensive Storage size Memory bandwidth -diemensional Image
Visual Media Processing Using MATLAB Beginner's Guide
Visual Media Processing Using MATLAB Beginner's Guide Learn a range of techniques from enhancing and adding artistic effects to your photographs, to editing and processing your videos, all using MATLAB
Digital image processing. Árpád BARSI BME Dept. Photogrammetry and Geoinformatics
Digital image processing Árpád BARSI BME Dept. Photogrammetry and Geoinformatics barsi.arpad@epito.bme.hu Part 1: (5/12/) Theory of image processing Part 2: (12/12/) Practice with software examples Main
4321 FIRST STREET LOREM ET $525,000
4321 FIRST STREET LOREM ET $525,000 UPMC DIGITAL PATHOLOGY CONSULTATION SERVICES OFFERS EXPERT SECOND-OPINION DIAGNOSTIC As the leader in Pathology Informatics for the last 20 years, the Department of
International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013 ISSN
2157 Automatic Color Form Dropout to Achieve Faster Document Processing Shital A. Dhanfule 1, Prashant N. Pusdekar 2, Vinaya V. Gohokar 3 1 PG, Student, Department of Electronics and Telecommunication
CS 376b Computer Vision
CS 376b Computer Vision 09 / 03 / 2014 Instructor: Michael Eckmann Today s Topics This is technically a lab/discussion session, but I'll treat it as a lecture today. Introduction to the course layout,
Introduction to BioImage Analysis
Introduction to BioImage Analysis Qi Gao CellNetworks Math-Clinic core facility 22-23.02.2018 MATH- CLINIC Math-Clinic core facility Data analysis services on bioimage analysis & bioinformatics: 1-to-1
COLOUR SEGMENTATION IN YARN-DYED WOVEN FABRIC IMAGES BY USING K- MEANS CLUSTERING Bekir Yildirim 1, Brigita Kolčavová Sirková 2
COLOUR SEGMENTATION IN YARN-DYED WOVEN FABRIC IMAGES BY USING K- MEANS CLUSTERING Bekir Yildirim 1, Brigita Kolčavová Sirková 2 1 Faculty of Engineering, University of Erciyes, Turkey 2 Faculty of Textile
Digital Pathology and Tissue-based Diagnosis. How do they differ?
Digital Pathology and Tissue-based Diagnosis. How do they differ? P. Hufnagl Institute of Pathology (Rudolf-Virchow-Haus). Humboldt University, Berlin? 10.12.2014 1 Structure of the talk Possible workflow
Automatic Locating the Centromere on Human Chromosome Pictures
Automatic Locating the Centromere on Human Chromosome Pictures M. Moradi Electrical and Computer Engineering Department, Faculty of Engineering, University of Tehran, Tehran, Iran moradi@iranbme.net S.
Deep Green. System for real-time tracking and playing the board game Reversi. Final Project Submitted by: Nadav Erell
Deep Green System for real-time tracking and playing the board game Reversi Final Project Submitted by: Nadav Erell Introduction to Computational and Biological Vision Department of Computer Science, Ben-Gurion
MEM455/800 Robotics II/Advance Robotics Winter 2009
Admin Stuff Course Website: http://robotics.mem.drexel.edu/mhsieh/courses/mem456/ MEM455/8 Robotics II/Advance Robotics Winter 9 Professor: Ani Hsieh Time: :-:pm Tues, Thurs Location: UG Lab, Classroom
Multilayer scanning enhances sensitivity of artificial intelligence-aided Mycobacterium tuberculosis detection
Multilayer scanning enhances sensitivity of artificial intelligence-aided Mycobacterium tuberculosis detection Yan Xiong Peking University First Hospital, China. yanxiong1109@163.com Ao Hou ao_sure@foxmail.com
IJRASET 2015: All Rights are Reserved
A Novel Approach For Indian Currency Denomination Identification Abhijit Shinde 1, Priyanka Palande 2, Swati Kamble 3, Prashant Dhotre 4 1,2,3,4 Sinhgad Institute of Technology and Science, Narhe, Pune,
Preprocessing of Digitalized Engineering Drawings
Modern Applied Science; Vol. 9, No. 13; 2015 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Preprocessing of Digitalized Engineering Drawings Matúš Gramblička 1 &
Received on: Accepted on:
ISSN: 0975-766X CODEN: IJPTFI Available Online through Research Article www.ijptonline.com AUTOMATIC FLUOROGRAPHY SEGMENTATION METHOD BASED ON HISTOGRAM OF BRIGHTNESS SUBMISSION IN SLIDING WINDOW Rimma
Automated workflow for Core Saturation experiment
Automated workflow for Core Saturation experiment 1. Introduction This tutorial will detail how to develop and use an automated workflow for a core flooding experiment. The workflow consists of a recipe
MRI Grid. The MRI Grid is a tool in MRI Cell Image Analyzer, that can be used to associate measurements with labeled positions on a board.
Abstract The is a tool in MRI Cell Image Analyzer, that can be used to associate measurements with labeled positions on a board. Illustration 2: A grid on a binary image. Illustration 1: The interface
CONTENT-BASED IMAGE RETRIEVAL OF BLOOD CELLS BASED ON COLOUR
CONTENT-BASED IMAGE RETRIEVAL OF BLOOD CELLS BASED ON COLOUR ALAGU RATHINA BAIRAVI FACULTY OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY UNIVERSITY OF MALAYA 2009 CONTENT-BASED IMAGE RETRIEVAL OF BLOOD
SCANNING GUIDELINES Peter Thompson (rev. 9/21/02) OVERVIEW
SCANNING GUIDELINES Peter Thompson (rev. 9/21/02) OVERVIEW WHAT S A SCANNER? A machine that lets you input an image into your and save it as a digital file to be enhanced or altered by image editing software
Appendix C: User manual for performing image analysis in experiment of monitoring E-coli growth. ImageJ user manual
Appendix C: User manual for performing image analysis in experiment of monitoring E-coli growth ImageJ user manual A. Recommended Browser for ImageJ Browser Version Internet Explorer 5+ Google Chrome 3
Number Plate Recognition Using Segmentation
Number Plate Recognition Using Segmentation Rupali Kate M.Tech. Electronics(VLSI) BVCOE. Pune 411043, Maharashtra, India. Dr. Chitode. J. S BVCOE. Pune 411043 Abstract Automatic Number Plate Recognition