Quintiles Intelligent Imaging Clear Vision for the Healthcare Industry

Slides:



Advertisements
Similar presentations
Introduction to Colour Management
Advertisements

Sept 13-15, 2004IHE Interoperability Workshop 1 Integrating the Healthcare Enterprise Consistent Presentation of Images Integration Profile Integrating.
Quintiles Intelligent Imaging Clear Vision for the Healthcare Industry DICOM Lookup Table Encoding Issues David Clunie I2I2.
Towards Clinically-relevant Standardization of Image Quality
Digital Image Processing
Grayscale Calibration Grayscale Standard Display Function
ECE 472/572 - Digital Image Processing Lecture 10 - Color Image Processing 10/25/11.
MovieLabs Proposals: An Extended Dynamic Range EOTF
Radiology Understanding Display Characteristics What You Should Know When Viewing Images from PACS.
 Image Characteristics  Image Digitization Spatial domain Intensity domain 1.
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
DREAM PLAN IDEA IMPLEMENTATION Introduction to Image Processing Dr. Kourosh Kiani
The Medicine Behind the Image DICOM Display Update: Color Presentation States Hanging Protocols Dr. David A. Clunie, MB.,BS., FRACR Chief Technology Officer.
1 Lecture 2 Main components of graphical systems Graphical devices.
Medical Image Display Bradley Hemminger School of Information and Library Science Department of Radiology, University of North Carolina, Chapel Hill
1 Color Segmentation: Color Spaces and Illumination Mohan Sridharan University of Birmingham
Color Mixing There are two ways to control how much red, green, and blue light reaches the eye: “Additive Mixing” Starting with black, the right amount.
Biomedical Tracers Biology 685 University of Massachusetts at Boston created by Kenneth L. Campbell, PhD.
Color Management Systems Problems –Solve gamut matching issues –Attempt uniform appearance Solutions –Image dependent manipulations (e.g. Stone) –Device.
University of British Columbia CPSC 414 Computer Graphics © Tamara Munzner 1 Color 2 Week 10, Fri 7 Nov 2003.
R+G+B vs. gray, LCD projector.
Display Issues Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico.
Color Fidelity in Multimedia H. J. Trussell Dept. of Electrical and Computer Engineering North Carolina State University Raleigh, NC
1/22/04© University of Wisconsin, CS559 Spring 2004 Last Time Course introduction Image basics.
Dye Sublimation Color Management
9/14/04© University of Wisconsin, CS559 Spring 2004 Last Time Intensity perception – the importance of ratios Dynamic Range – what it means and some of.
1 DICOM Imaging Pipeline Model Cor loef Philips Medical Systems.
Spectral contrast enhancement
DICOM Singapore Seminar:
09/05/02© University of Wisconsin, CS559 Spring 2002 Last Time Course introduction Assignment 1 (not graded, but necessary) –View is part of Project 1.
February 8, 2005IHE Europe Educational Event 1 Integrating the Healthcare Enterprise Consistent Presentation of Images Integration Profile Integrating.
Any questions about the current assignment? (I’ll do my best to help!)
IDL GUI for Digital Halftoning Final Project for SIMG-726 Computing For Imaging Science Changmeng Liu
Colours and Computer Jimmy Lam The Hong Kong Polytechnic University.
1 © 2010 Cengage Learning Engineering. All Rights Reserved. 1 Introduction to Digital Image Processing with MATLAB ® Asia Edition McAndrew ‧ Wang ‧ Tseng.
Consistent Presentation of Images - Integration Profile Ellie Avraham Kodak Health Imaging IHE Planning and Technical Committees.
Validation of Color Managed 3D Appearance Acquisition Michael Goesele Max-Planck-Institut für Informatik (MPI Informatik) Vortrag im Rahmen des V 3 D 2.
CS654: Digital Image Analysis Lecture 17: Image Enhancement.
Consistent Presentation of Images
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
03/05/03© 2003 University of Wisconsin Last Time Tone Reproduction If you don’t use perceptual info, some people call it contrast reduction.
CS6825: Color 2 Light and Color Light is electromagnetic radiation Light is electromagnetic radiation Visible light: nm. range Visible light:
Feb 7-8, 2007IHE Participant's Workshop 1 Integrating the Healthcare Enterprise Mammography Image – MAMMO Chris Lindop, GE Healthcare Co-Chair Radiology.
June 28-29, 2005IHE Interoperability Workshop 1 Integrating the Healthcare Enterprise Consistent Presentation of Images Integrating the Healthcare Enterprise.
The Reason Tone Curves Are The Way They Are. Tone Curves in a common imaging chain.
Page 1 Visual calibration for (mobile) devices Lode De Paepe GOAL: Calibrate the luminance response of a display (transfer function of the display.
CS654: Digital Image Analysis Lecture 29: Color Image Processing.
Three-Receptor Model Designing a system that can individually display thousands of colors is very difficult Instead, colors can be reproduced by mixing.
02-Gray Scale Control TTF. A TTF tells us how an imaging device relates the gray level of the input to the gray level of the output. P L Luminance, L.
DICOM INTERNATIONAL CONFERENCE & SEMINAR Oct 9-11, 2010 Rio de Janeiro, Brazil Managing Display Quality & Consistency Lawrence Tarbox, Ph.D. Washington.
EE 638: Principles of Digital Color Imaging Systems Lecture 14: Monitor Characterization and Calibration – Basic Concepts.
Consistent Presentation of Images Profile IHE North America Webinar Series 2008 Chris Lindop IHE Radiology GE Healthcare.
IHE Workshop – June 2006What IHE Delivers 1 Ellie Avraham Kodak Health Group IHE Planning and Technical Committees Consistent Presentation of Images Profile.
David Luebke 1 2/5/2016 Color CS 445/645 Introduction to Computer Graphics David Luebke, Spring 2003.
03/03/03© 2003 University of Wisconsin Last Time Subsurface scattering models Sky models.
Image Representation Last update st March Heejune Ahn, SeoulTech.
David Luebke2/23/2016 CS 551 / 645: Introductory Computer Graphics Color Continued Clipping in 3D.
Effects of Grayscale Window/Level on Breast Lesion Detectability Jeffrey Johnson, PhD a John Nafziger, PhD a Elizabeth Krupinski, PhD b Hans Roehrig, PhD.
ECE 638: Principles of Digital Color Imaging Systems Lecture 12: Characterization of Illuminants and Nonlinear Response of Human Visual System.
General Characteristics Neutral Characteristics: Viewing Illuminant Sensitivity Neutral Characteristics: Color Balance Grayscale Characteristics Color.
Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 Integrated Color Solutions A presentation.
Consistent Presentation of Images Integration Profile
Display Issues Ed Angel
CH. 6 Photographic Transparencies
Ellie Avraham Kodak Health Imaging
Perception and Measurement of Light, Color, and Appearance
Introduction to Computer Graphics with WebGL
Computer Vision Lecture 4: Color
DICOM Generic Pixel Presentation Pipeline
Consistent Presentation of Images
Presentation transcript:

Quintiles Intelligent Imaging Clear Vision for the Healthcare Industry DICOM Grayscale Standard Display Function David Clunie Quintiles Intelligent Imaging Clear Vision for the Healthcare Industry 2 1

Outline Inconsistent appearance of images Why is it a problem ? What are the causes ? Grayscale Standard Display Function The DICOM solution to the problem How it works How to implement it

Distributed Image Consistency Laser Printer Digital Modality Identical perceived contrast Workstation Workstation

Distributed Image Consistency Laser Printer Digital Modality Identical perceived contrast Workstation Workstation

Distributed Image Consistency Laser Printer Digital Modality Identical perceived contrast Workstation Workstation

Distributed Image Consistency Laser Printer Digital Modality Identical perceived contrast and color !! Workstation Workstation

What about color ? Consistency is less of an issue: US/NM/PET pseudo-color; VL true color ?? Consistency is harder to achieve Not just colorimetry (i.e. not just CIELAB) Scene color vs. input color vs. output color Gamut of devices much more variable Greater influence of psychovisual effects Extensive standards efforts e.g. ICC

Problems of Inconsistency VOI (window center/width) chosen on one device but appears different on another device Not all gray levels are rendered or are perceivable Displayed images look different from printed images …

Problems of Inconsistency VOI chosen on one display device Rendered on another with different display Mass expected to be seen is no longer seen mass visible mass invisible

Problems of Inconsistency 0.5 1.0 Not all display levels are perceivable on all devices 1.5 3.0

Problems of Inconsistency 0.5 1.0 Not all display levels are perceivable on all devices 1.5 3.0

Problems of Inconsistency Laser Printer Digital Modality Printed images don’t look like displayed images

Causes of Inconsistency Gamut of device Minimum/maximum luminance/density Characteristic curve Mapping digital input to luminance/density Shape Linearity Ambient light or illumination

Causes of Inconsistency Display devices vary in the maximum luminance they can produce Display CRT vs. film on a light box is an extreme example 1.0 .66

Monitor Characteristic Curves 0.1 1 10 100 50 150 200 250 300 Digital Driving Level Maximum Luminance Gamma Ambient Light

Towards a Standard Display Can’t use absolute luminance since display capabilities different Can’t use relative luminance since shape of characteristic curves vary Solution: exploit known characteristics of the contrast sensitivity of human visual system - contrast perception is different at different levels of luminance

Human Visual System Model contrast sensitivity assume a target similar to image features confirm model with measurements Barten’s model Grayscale Standard Display Function: Input: Just Noticeable Differences (JNDs) Output: absolute luminance

Standard Display Function Grayscale Standard Display Function 500 1000 1500 2000 2500 3000 3500 4000 4500 200 400 600 800 1200 JND Index

Standard Display Function Grayscale Standard Display Function 4500 4000 3500 Monitors Film 3000 2500 2000 1500 1000 500 200 400 600 800 1000 1200 JND Index

Standard Display Function .01 .1 1 10 100 1000 200 400 600 800 Grayscale Standard Display Function JND Index

Standard Display Function Grayscale Standard Display Function 1000 Film 100 10 Monitors 1 200 400 600 800 1000 .1 .01 JND Index

Perceptual Linearization JND index is “perceptually linearized”: same change in input is perceived by the human observer as the same change in contrast Is only a means to achieve device independence Does not magically produce a “better” image

Perceptual Linearization .01 .1 1 10 100 1000 200 400 600 800 Grayscale Standard Display Function JND Index Despite different change in absolute luminance Same number of Just Noticeable Difference == Same perceived contrast

Perceptual Linearization Ambient Light Display Display Modality Perception of Contrast By Human Visual System

Using the Standard Function Maps JNDs to absolute luminance Determine range of display minimum to maximum luminance minimum to maximum JND Linearly map: minimum input value to minimum JND maximum input value to maximum JND input values are then called “P-Values”

Monitor Characteristic Curve 0.1 10 100 50 150 200 250 300 Digital Driving Level Ambient Light

Standard Display Function .01 .1 1 10 100 1000 200 400 600 800 Grayscale Standard Display Function JND Index Maximum Luminance + Ambient Light Monitor’s Capability Minimum Luminance + Ambient Light Jmax == P-Value of 2n-1 Jmin == P-Value of 0

Standardizing a Display 0.1 1 10 100 50 150 200 250 DDL or P-Values Standard Characteristic Curve

Standardizing a Display Mapping P-Values to Input of Characteristic Curve (DDL’s) 50 100 150 200 250 300 P-Values DDL

Standardizing a Display Standard Display Function Standardized Display P-Values: 0 to 2n-1

Device Independent Contrast Standard Display Function Standard Display Function Standardized Display A Standardized Display B P-Values: 0 to 2n-1

So what ? Device independent presentation of contrast can be achieved using the DICOM Grayscale Standard Display Function to standardize display and print systems Therefore images can be made to appear the same (or very similar) on different devices

So what ? Images can be made to appear not only similar, but the way they were intended to appear, if images and VOI are targeted to a P-value output space New DICOM objects defined in P-values Old DICOM objects and print use new services (Presentation State and LUT)

Not so hard … If you calibrate displays or printers at all, you can include the standard function If you use any LUT at all, you can make it model the display function If you ignore calibration and LUTs totally (e.g. use window system defaults) the results will be inconsistent, mediocre and won’t use the full display range