Outline
• Display Processing
–
–
–
–
–
–
–
–
Digital Image Processing in
Radiography
Data preprocessing
ROI segmentation and analysis
Tonal rendering
Signal equalization
Edge restoration
Noise suppression
Collimation masking
Display compensation
• Image Processing Features
–
–
–
–
–
–
Xiaohui Wang, PhD
David H. Foos, MS
Health Group Research Laboratory
Eastman Kodak Company
1
Stationary grid detection and suppression
Long-length imaging
Dual energy imaging
Mammography
Oncology processing
Quality control testing
2
“Analog” Image Processing…
Screen/Film Radiography (S/F)
Density
3
Display Processing
2
1.0
1
0.8
4
5
6
Log Exposure
Modulation Transfer
0
0.6
Low Speed Screens
7
0.4
0.2
Film
Speed & Contrast
Screen
Speed & MTF
3
0
High Speed Screens
0
1
2
3
4
5
-1
Spatial Frequency (mm )
4
1
Digital Image Processing
Why Image Processing?
• Maintain the familiar characteristics of S/F
Digital Image Acquisition
– Provide a similar tonal rendering
– Restore desired sharpness
• Beyond the familiar
–
–
–
–
–
–
Digital Image Processing
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
Digital Image Display
5
6
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
•
Original Image
Tone Scale
Edge Restoration
Signal Equalization
Collimation Masking
3
Overexposed
100
2
Correctly exposed
10
1
Underexposed
1
0.01
7
4
Film-screen
(400 speed)
1,000
Film Optical Density
Relative intensity of PSL
PSP plate
10,000
0
0.1
1
10
Incident exposure, mR
100
8
2
Schematic Flow Chart of Display Processing
Original Image
Image
Capture
Tone Scale
Data
Preprocessing
ROI
Segmentation
& Analysis
Tone-Scale
Generation
Edge Restoration
Signal Equalization
Edge
Restoration
Collimation Masking
Signal
Equalization
Tonal
Rendering
Collimation
Masking
Display
Compensation
PACS
& Print
Noise
Suppression
Multi-Frequency Processing
9
10
Data Preprocessing
•
•
•
Data Preprocessing (cont.)
Linear-to-log conversion
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)
Shape of image histogram invariant to exposure
I0
I
f ( x)
1
f ( x)
log( I 0 / I ) ~
1
log( I ) const.
f (x)
Baseline
I
4x over exposure
4x under-ex posure
0
11
I0 e
1000
2000
3000
4000
5000
12
3
ROI Segmentation & Analysis
•
Extract diagnostically relevant ROIs
•
Analyze ROI characteristics
•
Derive the optimal display-rendering parameters
•
Include four basic steps
– Detect collimation mask
Direct
– Detect direct exposure
Exposure
– Extract anatomy regions
– Calculate key image descriptors
Segmentation – Collimation Mask
• Confine exposure regions
• Mask applied to collimated regions to reduce viewing flare
Collimation
Anatomy
“Method for recognizing multiple radiation fields in computed radiography,” X. Wang, J. Luo, R. Senn, and D. Foos,
Proc SPIE 3661, 1625-36, 1999.
13
14
Segmentation – Collimation Mask (cont.)
Segmentation – Collimation Mask (cont.)
•
• Collimation boundary pixels
– Edge profile analysis
– Transition segments classification
Multiple radiation field masking
– Optimal individual image processing
– Exam workflow improvement
• Candidate collimation blades
– Edge delineation
– FOM analysis
• straightness
• connectedness…
• Candidate configurations
• Select “best” configuration
– Parallelism, convexity, orthogonality, etc.
“Method for recognizing multiple radiation fields in computed radiography,” X. Wang, J. Luo, R. Senn, and D. Foos, Proc SPIE
3661, 1625-36, 1999.
J. Luo and R. Senn, “Collimation detection for digital radiography," Proc. SPIE 3034, 74-85 (1997).
15
16
4
Segmentation - Direct Exposure
Detection
Segmentation – Collimation Mask (cont.)
Aggressive
Failure
Exclude non-anatomical regions
within the collimation.
Conservative
Failure
• 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).
17
18
ROI Analysis - Key Image
Descriptors
Segmentation – Anatomy Extraction
Lateral Lumbar Spine ROI Selection
1
0.9
Normalized Frequency
0.8
0.7
0.6
act hist
0.5
cv. hist
0.4
0.3
0.2
Anatomy
0.1
Anatomy
0
Left pt
Far left pt
19
“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.
Right pt
Far right pt
20
5
Effect of Density Shift Pa ram eter
Tone-Scale Generation
4000
Brightness Adjustment
3500
Code Value Out
3000
• 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
– …
2500
2000
1500
shift = -0.5
1000
shift = 0
500
shift = 0.5
0
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Code Value In
22
Toe & Shoulder
Adjustment
Effect of Contrast Parameter
Contrast Adjustment
4000
Code Value Out
3500
3000
2500
2000
c = 0.8
1500
c = 1.3
1000
Effect of Toe and Shoulder Para meters
4000
3500
3000
Code Value Out
21
2500
2000
1500
T=0, S=0
c = 1.8
1000
500
0
T=0.3, S=0.7
T=0.6, S=1.4
500
0
1000
2000
3000
4000
5000
0
Code Value In
0
1000
2000
3000
4000
5000
Code V alue In
c
23
24
6
Visually Optimized Tone Scale
Visually Optimized Tone Scale (cont.)
Perceptual Linearity -
Radiologists prefer S/F systems with perceptually linear
sensitometric response.
Render ROI such that… equal physical contrast being
perceived as equal brightness by the observer across the full
brightness range
X-rays
2
3
Kodak Insight HC Thoracic
Imaging System
Kodak Insight Thoracic
Imaging System
25
26
Perceptual Tone Scale
Perceptual Tone Scale (cont.)
Equal Log (E)
Equal Brightness
dmax
Perceptually
Linearity
Tone Scale
Density – Luminance
(Physics)
dmin
flp
B=
frp
lp
Visual Perception
Model
Daly’s Global Cone Model
rp
Bm Ln
Ln + L0n
B = perceived brightness
L = luminance of the image area
Bm = scale factor
n = 0.7
L0 = 12.6*(0.2*Lw)0.63 + 1.083*10-5
Perception
Linearity
Lw = luminance of the reference white
H. Lee, S. Daly, and R. Van Metter, “Visual optimization of radiographic tone scale,” Proc. SPIE 3036, 118-129, 1996.
27
28
7
Signal Equalization
Tone Scale Failures
Effect of Contrast Parameter
4000
Code Value Out
3500
Balance between
contrast vs. latitude
3000
2500
Bone
2000
c = 0.8
Soft
c = 1.3
Tissue
c = 1.8
1500
1000
500
0
0
1000
2000
3000
4000
5000
Code Value In
Too Bright
Too Dark
Too Much
Contrast
Too Less
Contrast
29
Good Soft Tissue Contrast
Signal Equalization (cont.)
Good Bone Contrast
Signal Equalization (Kodak EVP)
• 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
Input Image &
PTONE LUT
•
•
•
•
•
EVP - Enhanced Visualization Processing (Kodak)
DRC - Dynamic Range Compression (Fuji)
Latitude Reduction (AGFA)
Tissue Equalization Processing (GE)
…
EVP GAIN
Original
Image
EVP
KERNEL SIZE
– 2D spatial processing
– Digital wedge filter
– Bearing different names:
31
Signal Equalization
30
Output Image &
PTONE LUT
-
Blurred
Image
PTONE
LUT
NEW
PTONE LUT
EVP GAIN and EVP DENSITY
E’(i,j) =
{ E(i,j)
K}+(1-
) Emid +
{ E(i,j) - ( E(i,j)
K)}
D(i,j) = [ E’(i,j) ].
32
“Enhanced latitude for digital projection radiography,” R. Van Metter and D. Foos, Proc. SPIE 3658, 468-483, 1999.
8
Signal Equalization (cont.)
Signal Equalization (cont.)
Increasing
Contrast,
…Decreasing
Latitude
11-11
8-8
5-5
11-11
8-11
5-11
Increasing
Contrast,
…Constant
Latitude
33
34
RSNA 2001 Education Exhibit
Observer “rendering preferences” established
through collaborative studies with university hospitals
(…to set default parameters for automatic processing)
Contrast
High
Low
Latitude
Detail
LogE
Contrast
D/ LogE =>
6.75
5.75
5
3.75
3.1
2.25
1.9
1.6
1.2
1
0.85
Latitude
rel. to
Ref.
Narrow
0.35
1.4
1.0
0.38
0.47
1.8
1.5
1.3
1.0
0.56
2.2
1.9
1.6
1.2
1.0
0.78
3.0
2.6
2.2
1.7
1.4
1.0
0.51
0.61
0.84
0.92
3.6
3.0
2.6
2.0
1.6
1.2
1.0
1.09
4.2
3.6
3.1
2.3
1.9
1.4
1.2
1.0
1.46
5.6
4.8
4.2
3.1
2.6
1.9
1.6
1.3
1.0
1.75
6.8
5.8
5.0
3.8
3.1
2.3
1.9
1.6
1.2
1.0
2.06
7.9
6.8
5.9
4.4
3.6
2.6
2.2
1.9
1.4
1.2
1.0
1.00
1.19
1.58
1.90
2.24
Latitude
Wide
“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.
35
36
9
Signal Equalization (cont.)
Equalization Processing Artifact
Optimal PA
View
Kodak T-Mat G
Film detail
contrast with 2X
extended latitude
Properly Processed
37
Halo
Artifact
Overprocessed
38
Edge Restoration
Edge Restoration (cont.)
• Selectively boost high-frequency signals based on
Modulation Transfer Function
–
–
–
–
–
1
CR Hi-Res Screen
CR Std Screen
0.8
High-frequency
signal is
suppressed by
system MTF.
CsI DR
Selenium DR
MTF
0.6
Selectively
boost high
frequency
0.4
0.2
• Multi-frequency processing (2D spatial)
–
–
–
–
–
0
0
1
2
3
4
Exam type
Brightness
Exposure
Diagnostic features
Capture device characteristics
5
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)
Spatial Frequency (cycles/mm)
39
40
10
Edge Restoration (cont.)
Edge Restoration (cont.)
M o d u la t io n T r a n s f e r F u n c t io n
Unsharp Mask Processing
1 .2
USM Gain
K = 5, g = 1.2
1
Output
Image
MTF
+
0 .6
K = 3, g = 1.5
0 .4
Original
Image
-
B a e li n e
US M 1
US M 2
0 .8
High-Freq.
Image
0 .2
0
USM
Kernel Size
0
Blurred
Image
1
2
3
4
5
S p a t ia l F r e q u e n c y (c y c le s /m m )
41
42
Edge Restoration (cont.)
Edge Restoration (cont.)
Halo
Artifact
Processing Artifact
Multi-Frequency
Processing
1
2
3
…
Original
Image
+
n
Edge-Enhanced
Image
n+1
Properly Processed
43
Overprocessed
44
11
Edge Restoration (cont.)
Noise Suppression
• 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.
Processing Artifact
Halo
Artifact
Properly Processed
• A noise suppression algorithm needs to reduce the appearance of
noise while preserving diagnostic detail.
Overprocessed
“Observer study of a noise suppression algorithm for computed radiography images,” M. Couwenhoven et al, Proc. SPIE 5749, 318-327, 2005.
45
46
Noise Suppression (cont.)
Noise Suppression (cont.)
Suppress the noise in low
signal areas and phase out
suppression in high signal
areas
High Signal Areas / Less
Dense Anatomy
Noise Suppression
•
•
•
•
2D spatial processing
Applies to high freq. signals
Signal dependent
Balance between sharpness &
noise
Low Signal Areas / More
Dense Anatomy
47
48
12
Display Compensation
Display Compensation (cont.)
Workstation Calibration Check
• Image pixel values can be mapped for different output devices
– Film printer (monochrome 1)
– Softcopy display (monochrome 2)
10.00
Image looks darker
throughout the dark regions
Luminance
• Both capture and output devices need to be configured properly
• Output device calibration is critical to optimal image display
–
–
–
–
100.00
1.00
Aim Luminance
Measured Luminance
Different dynamic range and response
CRT vs. flat-panel
Images from multi-vendors viewed at same PACS workstation
Archived images
0.10
0%
20%
40%
60%
80%
100%
SMPTE Patch
• DICOM Part 14 specifies grayscale display standard function
(GSDF)
• AAPM TG-18 specifies display QA & QC testing
Reference
49
Non-calibrated Display
50
Display Compensation (cont.)
Monitor Responses
Monitor
Response Examples
1000
Lum inance
100
Image displayed at CRT
looks brighter
CRT
Flat-Panel
10
Image Processing Features
1
0
1024
2048
3072
4096
0.1
DDL
Reference (flat-panel)
51
CRT
52
13
Stationary Grid Suppression
Automatic Grid Line Detection & Suppression
Moiré Pattern
A
B
C
(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
53
“Antiscatter stationary grid artifacts automated detection and removal in projection radiography imagery,”
I. Belykh and C. Cornelius, Proc. SPIE 2000.
54
Long-Length Imaging with CR
Long-Length Imaging
•
Screen Film Systems
35cm x130cm
and
35cm x 90cm
Cassettes
Computed Radiography
Currently Limited to
35cm x 43cm
•
•
•
55
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
56
14
Scan Individually
Storage Phosphor
Screens
Long-Length Imaging (cont.)
Image Processing
to Construct
Large Composite
“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.
57
58
Dual-Energy Imaging
Long-Length Imaging (cont.)
High kVp Radiograph
Tissue-Only
Bone-Only
• Measurements from CR equivalent to screen-film
– Angular
– Absolute distance
Images acquired at high and low energies are processed
to selectively cancel overlying tissues
• 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
Images courtesy of Dr. Jeff Siewerdsen at the Ontario Cancer Institute, Princess Margaret Hospital
and University of Toronto.”
59
60
15
Mammography Processing
Mammography Processing (cont.)
Recent advances in s/f mammography
S/F: A large dynamic range is needed to detect and display all
parts of the breast with good contrast
Min-R EV 150 system vs. Min -R 2000 system
Min -R EV 150 vs Min - R 2000 System
6.5
High Dmax and shoulder contrast
6.0
Results in greater overexposure latitude due to
higher upper scale contrast
5.5
5.0
4.5
4.0
2.5
Higher contrast
3.5
Better
of
2.0 visualization
Density
3.0
breast
1.5 parenchyma
Sharp toe
Results in “whiter whites ”, more “sparkle”,
improved visibility of microcalci fications
1.0
0.5
0.0
0.0
0.5
1.0
1.5
2.0
2.5
Log E
X-ray Sensitometry
KODAK MIN -R EV/
EV 150 Screen
61
KODAK MIN -R
2000/2000 Screen
62
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
Edge
Enhanced
Oncology Processing
Black surround
Signal Equalization
Simulation
Localization
Verification
Equalized
look
“Method for contrast-enhancement of digital portal images,” S. Young, W. E. Moore, D. H. Foos, US Patent US6836570 B2
63
64
16
Test Phantom for Kodak DIRECTVIEW
Total Quality Tool
Quality Control Testing
0.5 mm Cu
1.0 mm Al
180 cm
10.0 0.2 mR
@ 80 kVp
24 x 30 cm
35 x 43 cm
18 x 24 cm
65
66
KODAK TQT User Interface
Test Result Details
MTF (slow scan)
29.5%
67
68
17
Quality Control Testing (cont.)
Summary
• Tone scale processing establishes the overall image brightness and
global contrast
Automated Image Quality Control Tool
• Edge restoration enhances detail contrast
Precise and accurate quality control testing
• Signal equalization extends the latitude that can be visualized while
maintaining detail contrast
Highly reproducible quantitative results
Detects sub-visible changes in CR image quality
performance to initiate timely preventive
maintenance
Avoids hours of tedious and labor-intensive effort
with a highly automated procedure
Full data reporting in Excel format
• 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
69
70
Acknowledgements
•
•
•
•
Richard VanMetter
Lori L. Barski
William J. Sehnert
Lynn M. Fletcher-Heath
71
18