Digital Image Processing in RadiographyDigital Image Processing in Radiography Xiaohui Wang, PhD David H. Foos , MS Health Group Research Laboratory Eastman Kodak Company 2 Outline

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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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Log Exposure

Den

sity

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High Speed Screens

Low Speed Screens

Spatial Frequency (mm-1)

Mod

ula

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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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Incident exposure, mR

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Underexposed

Overexposed

Correctly exposed

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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

1

)(0

constIIIxf

eII xf

f (x)

I0

I

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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• 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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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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rmal

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act hist

cv. hist

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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Code Value In

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de

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shif t = -0.5

shif t = 0

shif t = 0.5

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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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T=0.3, S=0.7

T=0.6, S=1.4

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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

-

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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5-58-811-11

5-118-1111-11

Increasing Contrast,

…Decreasing Latitude

Increasing Contrast,

…Constant Latitude


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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

Con

tras

t

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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Properly Processed Overprocessed

Halo Artifact

Equalization Processing Artifact

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Modulation Transfer Function

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Spatial Frequency (cycles/mm)

MT

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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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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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Multi-Frequency Processing

Original Image

Edge-Enhanced Image

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n+1

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Halo ArtifactProcessing Artifact

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Halo Artifact

Processing Artifact


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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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Flat-Panel

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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Log E

Sharp toe

Higher contrast

High Dmax and shoulder contrast

Results in greater overexposure latitude due to higher upper scale contrast

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DensityBetter visualization of breast parenchyma

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

Documents

Digital Image Processing in RadiographyDigital Image Processing in Radiography Xiaohui Wang, PhD David H. Foos , MS Health Group Research Laboratory Eastman Kodak Company 2 Outline