1 DICOM Imaging Pipeline Model Cor loef Philips Medical Systems.

Slides:



Advertisements
Similar presentations
Types of Image Enhancements in spatial domain
Advertisements

Joe Luszcz Philips Ultrasound January 4, 2011 DICOM N-Dimensional Presentation State Description and Call for Participation.
CS Spring 2009 CS 414 – Multimedia Systems Design Lecture 4 – Digital Image Representation Klara Nahrstedt Spring 2009.
Digital Image Processing
Image Data Representations and Standards
Computational Biology, Part 23 Biological Imaging II Robert F. Murphy Copyright  1996, 1999, All rights reserved.
Image Processing Lecture 4
CS & CS Multimedia Processing Lecture 2. Intensity Transformation and Spatial Filtering Spring 2009.
Chapter 3 Image Enhancement in the Spatial Domain.
嵌入式視覺 Feature Extraction
Digital Image Processing
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
BYST Eh-1 DIP - WS2002: Enhancement in the Spatial Domain Digital Image Processing Bundit Thipakorn, Ph.D. Computer Engineering Department Image Enhancement.
Digital image processing Chapter 6. Image enhancement IMAGE ENHANCEMENT Introduction Image enhancement algorithms & techniques Point-wise operations Contrast.
Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli
Image enhancement in the spatial domain. Human vision for dummies Anatomy and physiology Wavelength Wavelength sensitivity.
Computer graphics & visualization HDRI. computer graphics & visualization Image Synthesis – WS 07/08 Dr. Jens Krüger – Computer Graphics and Visualization.
Image Enhancement To process an image so that the result is more suitable than the original image for a specific application. Spatial domain methods and.
6/9/2015Digital Image Processing1. 2 Example Histogram.
Sep 21, Fall 2005ITCS4010/ Computer Graphics Overview Color Displays Drawing Pipeline.
1 Comp300a: Introduction to Computer Vision L. QUAN.
Sep 21, Fall 2006IAT 4101 Computer Graphics Overview Color Displays Drawing Pipeline.
IAT 3551 Computer Graphics Overview Color Displays Drawing Pipeline.
Image Analysis Preprocessing Arithmetic and Logic Operations Spatial Filters Image Quantization.
Lecture 2. Intensity Transformation and Spatial Filtering
02/12/02 (c) 2002 University of Wisconsin, CS 559 Filters A filter is something that attenuates or enhances particular frequencies Easiest to visualize.
ECE 472/572 - Digital Image Processing Lecture 4 - Image Enhancement - Spatial Filter 09/06/11.
DICOM Singapore Seminar:
Image processing Second lecture. Image Image Representation We have seen that the human visual system (HVS) receives an input image as a collection of.
Image Formation. Input - Digital Images Intensity Images – encoding of light intensity Range Images – encoding of shape and distance They are both a 2-D.
Consistent Presentation of Images - Integration Profile Ellie Avraham Kodak Health Imaging IHE Planning and Technical Committees.
CS654: Digital Image Analysis Lecture 17: Image Enhancement.
Consistent Presentation of Images
EE663 Image Processing Dr. Samir H. Abdul-Jauwad Electrical Engineering Department King Fahd University of Petroleum & Minerals.
INT 840E Computer graphics Introduction & Graphic’s Architecture.
Lecture 3 The Digital Image – Part I - Single Channel Data 12 September
Digital image processing Chapter 3. Image sampling and quantization IMAGE SAMPLING AND IMAGE QUANTIZATION 1. Introduction 2. Sampling in the two-dimensional.
Image Display & Enhancement Lecture 2 Prepared by R. Lathrop 10/99 updated 1/03 Readings: ERDAS Field Guide 5th ed Chap 4; Ch 5: ; App A Math Topics:
Computer Graphics Chapter 6 Andreas Savva. 2 Interactive Graphics Graphics provides one of the most natural means of communicating with a computer. Interactive.
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
11/29/ Image Processing. 11/29/ Systems and Software Image file formats Image processing applications.
DIGITAL IMAGE. Basic Image Concepts An image is a spatial representation of an object An image can be thought of as a function with resulting values of.
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.
Digital Image Processing EEE415 Lecture 3
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.
CS 691B Computational Photography
Remote Sensing Image Enhancement. Image Enhancement ► Increases distinction between features in a scene ► Single image manipulation ► Multi-image manipulation.
Image Enhancement in Spatial Domain Presented by : - Mr. Trushar Shah. ME/MC Department, U.V.Patel College of Engineering, Kherva.
Lecture Reading  3.1 Background  3.2 Some Basic Gray Level Transformations Some Basic Gray Level Transformations  Image Negatives  Log.
Digital Image Processing Image Enhancement in Spatial Domain
Instructor: Mircea Nicolescu Lecture 5 CS 485 / 685 Computer Vision.
Geoprocessing and georeferencing raster data
DICOM INTERNATIONAL DICOM INTERNATIONAL CONFERENCE & SEMINAR April 8-10, 2008 Chengdu, China April 9, Application cases using the Enhanced XA SOP.
Mohammed AM Dwikat CIS Department Digital Image.
IS502:M ULTIMEDIA D ESIGN FOR I NFORMATION S YSTEM D IGITAL S TILL I MAGES Presenter Name: Mahmood A.Moneim Supervised By: Prof. Hesham A.Hefny Winter.
Image Enhancement in the Spatial Domain.
IS502:M ULTIMEDIA D ESIGN FOR I NFORMATION S YSTEM D IGITAL S TILL I MAGES Presenter Name: Mahmood A.Moneim Supervised By: Prof. Hesham A.Hefny Winter.
Consistent Presentation of Images Integration Profile
Supplement 189: Parametric Blending Presentation State Storage.
Computer Graphics Overview
Digital 2D Image Basic Masaki Hayashi
Soft Copy Presentation State
Histogram Histogram is a graph that shows frequency of anything. Histograms usually have bars that represent frequency of occuring of data. Histogram has.
DICOM Generic Pixel Presentation Pipeline
CSC 381/481 Quarter: Fall 03/04 Daniela Stan Raicu
Digital Image Processing
Consistent Presentation of Images
Intensity Transform Contrast Stretching Y ← u0+γ*(Y-u)/s
Presentation transcript:

1 DICOM Imaging Pipeline Model Cor loef Philips Medical Systems

2 Presentation Overview ScopeScope Image Processing and ViewingImage Processing and Viewing DICOM Pixel Processing modelDICOM Pixel Processing model Processing FunctionsProcessing Functions –Single Pixel –Pixel Set –Geometric transformations Color representationColor representation Annotations and view areaAnnotations and view area

3 Scope Pixel processing operations supported by DICOM conceptsPixel processing operations supported by DICOM concepts Contrast and Spatial ResolutionContrast and Spatial Resolution Single image view, with annotationsSingle image view, with annotations Monitor display and Film printingMonitor display and Film printing

4 Image Object Viewing device Columns Pixel Size Image Processing and Viewing Processing Rows Pixel ValueGray Level Annotations

5 Image Processing and Viewing Image Object Spatial Resolution Rows, Columns Pixel Size Contrast Resolution Pixel Value Range Number of Images Annotations Viewing Device Spatial resolution Rows, Columns Pixel Size Contrast Resolution Pixel Luminance/Density Range Number of Images Annotations

6 Original Acquired Image Modality LUT or Linear Transformation VOI LUT or Linear Transformation Normalize Physical Value Meaningful for Modality, manufacturer independent Application dependent subrange selection Stored Pixel Value DICOM Pixel Processing model (Non-DICOM) Acquisition processing Presentation LUT or Linear Transformation Gray Levels Normalize Perception P-Values Photometric Intepretation: Monochrome 1: min value -> White Monochrome 2: min value -> Black Polarity: Opposite of what’s specified with Photometric Interpretation (Print) Acquisition specific Image improvements

7 Processing Functions(1) Single Pixel, Single Image Image Object Spatial Resolution = Rows, Columns = Pixel Size = Contrast Resolution -> Pixel Value Range-> 1 Image= Viewing Device Spatial resolution Rows, Columns Pixel Size Contrast Resolution Pixel Luminance/Density Range 1 Image Processing

8 Max Stored Pixel Value Range Y=a.X+b Processing Functions(1) Single Pixel, Single Image Linear operation: Add, Subtract, Divide and Multiply by Constant Value Input X Range Output Y Range a b Output=RSxPixel + RI Max Rescale Type Range Rescale Slope Rescale Intercept DICOM

9 Processing Functions(1) Non-Linear operation: Output=F(Input) Y=F(X) Input X Range Output Y Range Y[n]=F[Start Value + n-1] N Number of Entries Output Y Range [0..2 B -1] DICOM: Modality LUT, VOI LUT Start Value

10 Processing Functions(1) Histogram operations: Contrast Stretching, Contrast Compression Histogram is pixel intensity distribution Frequency Pixel value ( intensity )

11 Processing Functions(1) Histogram, Contrast Stretching Applied to an Image to stretch (part of) a histogram to fill the full dynamic range of the display device. DICOM: VOI Window Width/Window Center Max Output Range (Dynamic Range Display Device) Max Input range No values WC WW

12 Processing Functions(1) Histogram, Contrast Compression Applied to an Image to suppress a part of the dynamic range of the display device. DICOM: VOI Window Width/Window Center Max Output Range (Dynamic Range Display Device) Max Input range No values WC WW

13 Processing Functions(2) Set Pixels, Single Image or Multiple Frames Image Object Spatial Resolution = Rows, Columns = Pixel Size = Contrast Resolution -> Pixel Value Range-> >=1 Images-> Viewing Device Spatial resolution Rows, Columns Pixel Size Contrast Resolution Pixel Luminance/Density Range 1 Image

14 Processing Functions(2) Convolution P1 P2 P3 P4 P5 P6 P7 P8 P9 C1 C2 C3 C4 C5 C6 C7 C8 C9 X DICOM: Convolution operations not supported. Could become part of the Advanced Presentation State SOP Class. Example of Industry use: Edge Enhancement: Output=Input + Gain*(Input-Convoluted_Region) Image In Image Out Kernel

15 Processing Functions(2) Add,Subtract and Average operations on multiple Images, Frames Operations on 1 pixel in multiple frames, and generation of output pixel based on two (processed) input pixel values. Images/Frames Sum N _

16 Processing Functions(2) Sum N Sum N _ Mask Frames Applicable Contrast Frames Pixel Intensity Relationship is LOG DICOM XA Multi-frame supports subtraction

17 Processing Functions(3) Geometry operations: Scaling, Rotate/Flip/Displayed Area Single Image Image Object Spatial Resolution -> Rows, Columns -> Pixel Size -> Contrast Resolution -> Pixel Value Range-> 1 Image= Viewing Device Spatial resolution Rows, Columns Pixel Size Contrast Resolution Pixel Luminance/Density Range 1 Image

18 Processing Functions(3) Scaling, Zoom-in, Zoom-out No more 1-to-1 mapping of pixels => “holes” and “overlaps” in pixel view area Need for interpolation. Interpolation types: Replicate, Bilinear, Cubic DICOM: Print has Magnification Type with the mentioned interpolation options. Requested Image Size, Rows/Columns and Pixel Aspect Ratio.

19 Processing Functions(3) Replicate: Bilinear: Cubic: P[i]P[i+1] X P[i]P[i+1] X P[i]P[i+1] X P[i-1]P[i+2]

20 Processing Functions(3) Rotate/Flip Rotate may result in rescaling operation DICOM: Presentation State has Rotate ( 90,180, 270) and Horizontal Flip Rotate Horizontal Flip

21 Processing Functions(3) Pixel size Different pixel size may result in the need to interpolate DICOM: CT/MR has Pixel Spacing, absolute Row Height/Column Width X-Ray has Pixel Aspect Ratio, relative Row Height/Column Width and Imager Pixel Spacing ( absolute, on detector plate ) Print has Requested Image Size, x-dimension in mm of image in Image Box, and Image Display Format. Printer Pixel Spacing retrieved with new SOP Class: Printer Configuration Processing Pixel Aspect Ratio 2/1

22 Color representation DICOM: Photometric Interpretation: - Palette Color, 1 sample value with 3 Palette Color LUTs that define R,G,B - RGB, 3 sample values for R,G,B Red-Palette Color LUT Green-Palette Color LUT Blue-Palette Color LUT Sample pixel value

23 Annotations and view area Text and Vector graphics, added to the Image pixels Currently in DICOM only Overlay and Curve. In Presentation State directly text and vector graphics, both in Image space and Display Device coordinate space.

24 X (Anchor) Bounding Box Text Polyline (Filled) Vector Graphics Annotations

25 DICOM: Overlays and Curves ( in Image space ) Overlay: ROI or Graphic - 1 bit, on-off - Origin, Rows, Columns - Type: Graphics or ROI - Max 16 planes - May be multi-frame Curve Type: ROI or POLY - List of (x,y) coordinates Image Overlay Origin

26 Annotations and view area Shutter, geometric mask applied on the image during display to neutralize the display of any pixels located outside the shutter shape. DICOM has the following shutter shapes: Rectangular, Circular, Polygonal