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Digital Image Processing in Radiography
Xiaohui Wang, PhD
David H. Foos, MSHealth Group Research Laboratory
Eastman Kodak Company
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Outline• Display Processing
– Data preprocessing– ROI segmentation and analysis– Tonal rendering – Signal equalization– Edge restoration– Noise suppression– Collimation masking– Display compensation
• Image Processing Features– Stationary grid detection and suppression– Long-length imaging– Dual energy imaging– Mammography– Oncology processing– Quality control testing
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Display Processing
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“Analog” Image Processing…
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Low Speed Screens
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Screen/Film Radiography (S/F)
Film ⇒ Speed & ContrastScreen ⇒ Speed & MTF
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Digital Image Processing
Digital Image Acquisition
Digital Image Processing
Digital Image Display
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Why Image Processing?
• Maintain the familiar characteristics of S/F– Provide a similar tonal rendering– Restore desired sharpness
• Beyond the familiar– Automatically adjust for errors in exposure– Automatically accommodate changes in latitude– Increase the range of exposures visualized– Enhance selected spatial frequencies– Highlight regions of interest (ROI)– Assist the radiologist to find features of interest
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Display Processing
• Transform digital radiography raw data to display values for presentation using a workstation or film printer, automatically, robustly, and consistently.
• Components of Image Quality
– Latitude
– Contrast
– Brightness
– Sharpness
– Noise
PSP plate
Film-screen(400 speed)
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Underexposed
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Original Image
Tone Scale
Edge Restoration
Signal Equalization
Collimation Masking
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Original Image
Tone Scale
Edge Restoration
Signal Equalization
Collimation Masking
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Image Capture
Data Preprocessing
ROI Segmentation & Analysis
Tone-Scale Generation
Edge Restoration
Signal Equalization
Noise Suppression
Tonal Rendering
Collimation Masking
Display Compensation
Schematic Flow Chart of Display Processing
PACS & Print
Multi-Frequency Processing
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Data Preprocessing
• Image reformation– Composition (dual-side CR reading)– Decomposition (dual energy)– Resize
• Signal filtering– Gain, offset, and bad pixel correction (DR)– Noise reduction– Stationary grid artifact suppression
• Data space conversion– Linear to logarithmic (const. object contrast vs. pixel value)– Linear to square root (const. quantum noise vs. pixel value)
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Data Preprocessing (cont.)
.)log(~)/log()( 10
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)(0
constIIIxf
eII xf
f (x)
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Linear-to-log conversion
Shape of image histogram invariant to exposure
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Baseline
4x over exposure
4x under-exposure
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ROI Segmentation & Analysis
• Extract diagnostically relevant ROIs
• Analyze ROI characteristics
• Derive the optimal display-rendering parameters
• Include four basic steps
– Detect collimation mask– Detect direct exposure– Extract anatomy regions– Calculate key image descriptors
Collimation
Direct Exposure
Anatomy
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• Confine exposure regions
• Mask applied to collimated regions to reduce viewing flare
“Method for recognizing multiple radiation fields in computed radiography,” X. Wang, J. Luo, R. Senn, and D. Foos, Proc SPIE 3661, 1625-36, 1999.
Segmentation – Collimation Mask
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• Collimation boundary pixels– Edge profile analysis– Transition segments classification
• Candidate collimation blades– Edge delineation– FOM analysis
• straightness • connectedness…
• Candidate configurations• Select “best” configuration
– Parallelism, convexity, orthogonality, etc.
J. Luo and R. Senn, “Collimation detection for digital radiography," Proc. SPIE 3034, 74-85 (1997).
Segmentation – Collimation Mask (cont.)
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Segmentation – Collimation Mask (cont.)
• Multiple radiation field masking
– Optimal individual image processing
– Exam workflow improvement
“Method for recognizing multiple radiation fields in computed radiography,” X. Wang, J. Luo, R. Senn, and D. Foos, Proc SPIE 3661, 1625-36, 1999.
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Segmentation – Collimation Mask (cont.)
Aggressive Failure
Conservative Failure
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Segmentation - Direct Exposure Detection
• Compensate– Radiation field non-uniformity
– X-ray scatter
– Multiple exposures
• Transition segment analysis – Line profile analysis
– Background transitions characterized by slope and extent
– Background pixel histogram analysis
– Spatial correlation of exposure variations
L. Barski and R. Senn “Determination of direct x-ray exposure regions in digital medical imaging,” U.S. Patent 5,606,587 (1997).
Exclude non-anatomical regions within the collimation.
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Anatomy Anatomy
Segmentation – Anatomy Extraction
“Automatic and exam-type independent algorithm for the segmentation and extraction of foreground, background, and anatomy regions in digital radiographic images,” X. Wang, H. Luo, Proc. SPIE 5370, 1427-1434, 2004. 20
Lateral Lumbar Spine ROI Selection
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ROI Analysis - Key Image Descriptors
Far left pt
Left pt
Far right pt
Right pt
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Tone-Scale Generation
• Render image with proper brightness and contrast
– Calculate average exposure within ROI
– Automatically adjust for errors in exposure
• Sigmoid curve shape in general
– Curve shift (brightness adjustment)
– Curve rotation (contrast adjustment)
– Toe & shoulder adjustment
• Bear different names
– Kodak: PTS (Perceptual Tone Scale)
– Fuji: Gradation Processing
– …
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Brightness AdjustmentEffect of Density Shift Pa rameter
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Contrast AdjustmentEffect of Contrast Parameter
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Effect of Contrast Parameter
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Code Value In
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Toe & Shoulder Adjustment
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Effect of Toe and Shoulder Parameters
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Visually Optimized Tone Scale
X-rays
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Perceptual Linearity -
Render ROI such that… equal physical contrast being perceived as equal brightness by the observer across the full brightness range
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Visually Optimized Tone Scale (cont.)
Kodak Insight HC Thoracic Imaging System
Kodak Insight Thoracic Imaging System
Radiologists prefer S/F systems with perceptually linear sensitometric response.
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Perceptual Tone Scale
lp rp
dmax
dminfrp
Visual Perception
Model
flp
Perception
Linearity
Density – Luminance
(Physics)
Perceptually
Linearity
Tone Scale
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Perceptual Tone Scale (cont.)
Equal Log (E) Equal Brightness
H. Lee, S. Daly, and R. Van Metter, “Visual optimization of radiographic tone scale,” Proc. SPIE 3036, 118-129, 1996.
B = Bm Ln
Ln + L0n
B = perceived brightnessL = luminance of the image areaBm = scale factorn = 0.7
L0 = 12.6*(0.2*Lw)0.63 + 1.083*10-5
Lw = luminance of the reference white
Daly’s Global Cone Model
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Tone Scale Failures
Too Bright Too Dark Too Much Contrast
Too Less Contrast
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Signal Equalization Effect of Contrast Parameter
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Effect of Contrast Parameter
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Balance between contrast vs. latitude
Good Soft Tissue Contrast Good Bone ContrastSignal Equalization
Soft Tissue
Bone
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Signal Equalization (cont.)
• Automatically accommodate changes in latitude
– Compress the image-signal dynamic range such that all information within ROI can be rendered with optimal contrast
• Increase the range of exposures visualized
• Reduce exposure re-take and improve workflow
• Signal equalization processing is– 2D spatial processing
– Digital wedge filter
– Bearing different names:• EVP - Enhanced Visualization Processing (Kodak)
• DRC - Dynamic Range Compression (Fuji)
• Latitude Reduction (AGFA)
• Tissue Equalization Processing (GE)
• … 32
Input Image &PTONE LUT
OriginalImage
PTONELUT
BlurredImageEVP
KERNEL SIZE
EVP GAIN and EVP DENSITY
NEWPTONE LUT
Output Image &PTONE LUTEVP GAIN
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E’(i,j) = { E(i,j) K } + ( 1 - ) Emid + { E(i,j) - ( E(i,j) K ) }
D(i,j) = [ E’(i,j) ].
Signal Equalization (Kodak EVP)
“Enhanced latitude for digital projection radiography,” R. Van Metter and D. Foos, Proc. SPIE 3658, 468-483, 1999.
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Signal Equalization (cont.)
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5-58-811-11
5-118-1111-11
Increasing Contrast,
…Decreasing Latitude
Increasing Contrast,
…Constant Latitude
Signal Equalization (cont.)
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RSNA 2001 Education Exhibit
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Detail Contrast
D/ LogE
Latitude LogE
=> 0.35 0.47 0.56 0.78 0.92 1.09 1.46 1.75 2.066.75 1.4 1.8 2.2 3.0 3.6 4.2 5.6 6.8 7.95.75 1.5 1.9 2.6 3.0 3.6 4.8 5.8 6.8
5 1.0 1.3 1.6 2.2 2.6 3.1 4.2 5.0 5.93.75 1.0 1.2 1.7 2.0 2.3 3.1 3.8 4.43.1 1.0 1.4 1.6 1.9 2.6 3.1 3.6
2.25 1.0 1.2 1.4 1.9 2.3 2.61.9 1.0 1.2 1.6 1.9 2.21.6 1.0 1.3 1.6 1.91.2 1.0 1.2 1.4
1 1.0 1.20.85 1.0
Latitude rel. to Ref. 0.38 0.51 0.61 0.84 1.00 1.19 1.58 1.90 2.24
Latitude
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tras
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High
Low
Narrow Wide
Observer “rendering preferences” establishedthrough collaborative studies with university hospitals(…to set default parameters for automatic processing)
“Optimal display processing for digital radiography,” M. Flynn, M. Couwenhoven, W. Eyler, B. Whiting, E. Samei, D. Foos, R. Slone, and E. Marom, Proc. SPIE 4319-36, 2001.
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Optimal PA ViewKodak T-Mat G Film detail contrast with 2X extended latitude
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Signal Equalization (cont.)
Properly Processed Overprocessed
Halo Artifact
Equalization Processing Artifact
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Modulation Transfer Function
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CR Hi-Res Screen
CR Std Screen
CsI DR
Selenium DR
Edge Restoration
High-frequency signal is suppressed by system MTF.
Selectively boost high frequency
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Edge Restoration (cont.)
• Selectively boost high-frequency signals based on– Exam type
– Brightness
– Exposure
– Diagnostic features
– Capture device characteristics
• Multi-frequency processing (2D spatial)– Kodak: EVP & USM (Enhanced Visualization Processing & UnSharp Mask)
– AGFA: MUSICA (MUlti-Scale Image Contrast Amplification)
– Konica: Hybrid (Mutil-Resolution Hybrid Processing)
– Fuji: USM & MFP (Multi-Objective Frequency Processing)
– Philips: UNIQUE (UNified Image QUality Enhancement)
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OriginalImage
BlurredImageUSM
Kernel Size
Output Image
USM Gain
- High-Freq.Image
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Edge Restoration (cont.)
Unsharp Mask Processing
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Edge Restoration (cont.)M o d u la t io n T ra n s fe r F u n c t io n
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…
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Edge Restoration (cont.)
Multi-Frequency Processing
Original Image
Edge-Enhanced Image
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Edge Restoration (cont.)
Properly Processed Overprocessed
Halo ArtifactProcessing Artifact
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Properly Processed Overprocessed
Halo Artifact
Processing Artifact
Edge Restoration (cont.)
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• It is desired to drive toward lower x-ray exposures to reduce patient dose
• The appearance of noise increases as exposure level is decreased
• A predominant source of noise in digital radiography is generally the quantum noise.
• Noise suppression should be signal dependent, it should be applied only to areas of the image that have a low SNR.
• A noise suppression algorithm needs to reduce the appearance of noise while preserving diagnostic detail.
Noise Suppression
“Observer study of a noise suppression algorithm for computed radiography images,” M. Couwenhoven et al, Proc. SPIE 5749, 318-327, 2005.
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Suppress the noise in low signal areas and phase out suppression in high signal areas
High Signal Areas / Less Dense Anatomy
Low Signal Areas / More Dense Anatomy
Noise Suppression (cont.)
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Noise Suppression (cont.)
Noise Suppression
• 2D spatial processing• Applies to high freq. signals• Signal dependent• Balance between sharpness &
noise
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Display Compensation
• Image pixel values can be mapped for different output devices– Film printer (monochrome 1)
– Softcopy display (monochrome 2)
• Both capture and output devices need to be configured properly
• Output device calibration is critical to optimal image display– Different dynamic range and response
– CRT vs. flat-panel
– Images from multi-vendors viewed at same PACS workstation
– Archived images
• DICOM Part 14 specifies grayscale display standard function (GSDF)
• AAPM TG-18 specifies display QA & QC testing
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Display Compensation (cont.)Workstation Calibration Check
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SMPTE Patch
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Measured Luminance
Image looks darker throughout the dark regions
Reference Non-calibrated Display
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Display Compensation (cont.)Monitor Responses
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Image displayed at CRT looks brighter
Reference (flat-panel) CRT
Monitor Response Examples
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Image Processing Features
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(A) CR chest image minified by a factor of 0.2 showing a grid-caused Moiré pattern(B) Filtered image fragment without Moiré pattern (C) Difference image
AA BB CC
Stationary Grid Suppression
Moiré Pattern
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Automatic Grid Line Detection & Suppression
“Antiscatter stationary grid artifacts automated detection and removal in projection radiography imagery,”I. Belykh and C. Cornelius, Proc. SPIE 2000.
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Screen Film Systems35cm x130cm
and 35cm x 90cm
Cassettes
Computed RadiographyCurrently Limited to
35cm x 43cm
Long-Length Imaging
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• Multiple 35cm x 43cm CR screens arranged in alternating and partially overlapping fashion for patient imaging
• Storage phosphor screens scanned individually
• Image processing software used to automatically
– Determine CR screen sequence and orientation
– Correct for magnification, translation, and rotation among individual screens
– Remove redundant image data in the overlap region
– Construct (stitch) a large geometrically accurate composite
– Eliminate the seam lines in the composite image
• Composite image stored to PACS for interpretation
Long-Length Imaging with CR
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Scan IndividuallyStorage Phosphor
Screens
Image Processing to Construct
Large Composite
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Long-Length Imaging (cont.)
“Fully automatic and reference-marker-free image stitching method for full-spine and full-leg imaging with computed radiography,” X. Wang, D. H. Foos, J. Doran, and M. K. Rogers, Proc. SPIE 5368, p361-369, 2004.
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• Measurements from CR equivalent to screen-film– Angular
– Absolute distance
• Visual quality of CR superior to S/F– Wide exposure latitude of CR
– Equalization processing
• 35% retake rate with S/F reduced to 0% retake rate with CR– 43 cm width
– Equalization processing
Long-Length Imaging (cont.)
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Images acquired at high and low energies are processed to selectively cancel overlying tissues
High kVp Radiograph Tissue-Only Bone-Only
Images courtesy of Dr. Jeff Siewerdsen at the Ontario Cancer Institute, Princess Margaret Hospital and University of Toronto.”
Dual-Energy Imaging
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Mammography Processing
S/F: A large dynamic range is needed to detect and display all parts of the breast with good contrast
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Mammography Processing (cont.)
Min-R EV 150 system vs. Min -R 2000 system
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Sharp toe
Higher contrast
High Dmax and shoulder contrast
Results in greater overexposure latitude due to higher upper scale contrast
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X-ray Sensitometry
KODAK MIN -R 2000/2000 Screen
KODAK MIN -R EV/ EV 150 Screen
Results in “whiter whites”, more “sparkle”, improved visibility of microcalci fications
Min-R EV 150 vs Min -R 2000 System
Recent advances in s/f mammography
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Mammography Processing
Digital Mammography
• Wide dynamic range (> 1000:1) captures all the image information
• Edge restoration enhances image details of different sizes
• Equalization processing compresses image latitude while maintaining contrast
S/F look Equalized look
Edge Enhanced
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Oncology Processing
SimulationLocalizationVerification
“Method for contrast-enhancement of digital portal images,” S. Young, W. E. Moore, D. H. Foos, US Patent US6836570 B2
Black surroundSignal Equalization
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180 cm
0.5 mm Cu1.0 mm Al
10.0 0.2 mR@ 80 kVp
Quality Control Testing
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Test Phantom for Kodak DIRECTVIEW Total Quality Tool
35 x 43 cm24 x 30 cm
18 x 24 cm
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KODAK TQT User Interface
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Test Result Details
29.5%
MTF (slow scan)
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Automated Image Quality Control Tool
Precise and accurate quality control testing
Highly reproducible quantitative results
Detects sub-visible changes in CR image quality performance to initiate timely preventive maintenanceAvoids hours of tedious and labor-intensive effort with a highly automated procedure
Full data reporting in Excel format
Quality Control Testing (cont.)
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Summary• Tone scale processing establishes the overall image brightness and
global contrast
• Edge restoration enhances detail contrast
• Signal equalization extends the latitude that can be visualized while maintaining detail contrast
• Edge restoration, signal equalization, and noise suppression are 2D spatial processing
• Multi-frequency has been widely adopted, yet users should be aware of processing artifacts
• Display processing are becoming easier and more intuitive to use
• Image processing provides many new features unique to digital capture
• Image processing can provide many automations to improve work efficiency
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Acknowledgements
• Richard VanMetter• Lori L. Barski• William J. Sehnert• Lynn M. Fletcher-Heath