Basics of Radiography Testing and Image Processing

7/29/2019 Basics of Radiography Testing and Image Processing

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Basics of Radiography Testing

and Image Processing

B.Venkatraman*, Uwe Ewert**, M.Menaka* and

**

*Indira Gandhi Centre for Atomic Research India

** BAM Berlin German

Preconference Workshop 18 WCNDT, Durban, April 14, 2012


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Structure of the Talk

Basic Principle of Radiographic Testing

factors affecting radiographic image

ee or mage process ng Ima e arameters and terminolo ies

Image Processing Methods

Applications on radiographic images


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Rad iog raphyRad iog raphy

116 yrs old Technique is identical.Improvements in sources and detectors.

Object placed between

source and detector (film).

Differential absorption ofradiation. I = I e-x

The film darkness

the amount of radiation

reachin the film

X-ray film

= less ex osure

through the test object.

= more exposureTop view of developed film


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Sources Detectors

,

Microfocus

....

Image intensifiersImage intensifiers

Linac XX--ray sensitiveray sensitive vidiconsvidicons

Fluorescent screens +Fluorescent screens +

etatron Synchrotron

CCDCCD

Isotopic Sources

Ir192, Co60, Tm170, Digital Flat PanelsDigital Flat Panels

Yb169, Cs137, Se75 near o e rraynear o e rray


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PARAMETERS AFFECTING RADIOGRAPHIC IMAGING

MEASURED THROUGH SENSITIVITY

SUBJECT FILM CONTRAST* DEFINITION

ENERGY IMAGE QUALITY

INTENSITY SIGNAL TO NOISE RATIO

FOCAL SPOT SIZE BASIC SPATIAL RESOLUTION

DETECTOR CHARACTERISTICS

TRADITIONAL APPROACHES - COMPENSATION PRINCIPLES


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CONFIGUARATION OF IMAGE PROCESSING SYSTEM

X-RAYS TRANSMITTED THR. OBJECTS

FILMS REAL TIME SYSTEMS DIGITAL

DIGITISER(SCANNER, A/D

A/D CONVERTOR,(LUT)

DIGITALINTERFACE

CONVERTOR, LUT)

COMPUTER + GRAPHICS CARDS + MEMORIES + DISPLAY DEVICES


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DIGITAL IMAGE REPRESENTATION

Images - 2-D intensity function f(x,y), x and y : spatial coordinates and f:proportional to the intensity / brightness (gray level) of the image.

rows and columns and quantized levels.

DIGITAL IMAGE:

Image

(x,y) Digital

Image

n f(x,y)

columns and finite

gray levels

It is a matrix with M rows and N columns. Each matrix element has atypical gray level value ranging from 0-255 (8 bit).

( , )r x y

( , ) ( , )

( , )

f x y g x y

b x y


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IMAGE FORMATS AND TYPES

Converting the acquired image into computer acceptabledigitized from.

.

Stored formats

TIFF (Tagged Image File Format)

JPEG (Joint Photographic Expert Group)

XWD (X Windows Dump)PNG (Portable Network Graphics)

Image types

Intensity image (data matrix with intensities scaled to represent grayscale)

nary mage og ca array o s an s on y ac an w e

RGB image( three separate matrices (Red, Green and Blue)


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IMAGE PROCESSING

Image processing refers to a set of

existing image f into new image g.

.


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DIGITAL IMAGE PROCESSING

Processes whose inputs and outputs are images in which werender detail more clearly and extract attributes from images up

Image Compression (for effective Morphological Image Processingstorage & transmission) (image component extraction)

Color Image Processing

ProcessingImage Segmentation

Knowledge baseDescription

(Quantitative

Image Restoration

Recover ima e

opera ons n co or oma n

Image Enhancement (High-

light Region of Interest)

Object Recognition

(assign label to

Image Acquisition objects)


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Image Processing Operations typical in RT

Image Processing

Nei hbourhood

- Point Operations

-

/Matrix operations- Smoothing and

- Contrast

- LUT

arpness

- statistical filters

- Gamma value,

- Histogram

, ,

- directional filters- radient filter

- edge detection


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Point Operations

rocess n pa a oma n s eno e y e express onG (x,y) = T [ F(x,y) ]

F(x,y) is Input Image, G (x,y) is Processed Image

, .

Gray Level Transformation:

= r

The effect is to produce an image of higher contrast than the original by

.

This results in Contrast Stretching.s

mDark Light

Dark r


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Contrast and Brightness Enhancement

Raw Image

Raw Image After Brightness and Contrast Adjustment


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Histogram Equalisationk

Sk = T(rk) = k = 0,1, 2 ..L 1, rk is the kth gray levelnkis the number of pixels in the image having gray level rk

jj nn

0/

The idea of histogram equalization is that the pixels should be

distributed evenly over the whole intensity range, i.e. the aim is to

3x 10

5

transform the image so that the output image has a flathistogram.

1

1.5

2

2.5

0 50 100 150 200 250 3000

0.5

Original radiograph

2

2.5

3x 10

5

0 50 100 150 200 250 3000

0.5

1

1.5

Histogram enhanced


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Neighbourhood / Matrix Operations

Z1 Z2 Z3 Response R of a pixel is

Z4 Z5 Z6W4 W5 W6

R=

wizi i=1, 2, ., mn

Window is moved in such a way

Z7 Z8 Z9W7 W8 W9

that W5 is placed on the pixel of

interest.

Ima e divided into nei hborhoods.

(m=n=3) Filter or window

(m=n=3)

Sub images called filter or mask or kernel or template or window is appliedon the neighborhood of the images.


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Smoothing

Original Image

P(i,j) = average [P(i,j), P(i-1,j-1), P(i+1,j+1),P(i+1,j-1), P(i-1,j+1)]

Smooth : Blurs the active

image or selection. This filterrep aces eac p xe w t t eaverage of its 33neighborhood.

Smoothened Image


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Averaging Filters

Averaging filters Replace the gray level of the candidate

cropped image

pixel by the average gray level of theneighborhood pixels.

Eliminate impulse noise andegra a ons appear ng as n s r pes.

Blur the image to get a grossrepresentation of objects of interest as

Fig. a Original Radiograph

the background while objects of interestare easy to detect.

Enhanced radiograph using average filter

1 1 1

1 1 11/9

1 2 1

2 4 21/16

1 1 1

Averaging Mask

1 2 1

Weighted AveragingMask

.


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Sharpening Filters - Spatial Domain

To highlight fine detail in an image or to enhance detail that hasbeen blurred either in error or during image acquisition

Fine details result in discontinuities in an image such as points,

edges and lines

Operator used must result in zero values at flat segments and.

First order derivative (Gradient operators)

Mathematical equation : f/ x=f(x+1,y)-f(x,y), f/ y=f(x,y+1)-f(x,y)

Filter masks : Roberts, Sobel and Prewitt

Mathematical e uation : 2f/ x2=f x+1 +f x-1 -2f x2f/ y2=f(x,y+1)+f(x,y-1)-2f(x,y)

Filter mask : Laplacian


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Filtering Noise Removal

-


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Edge EnhancementConvolve filter Variance filter Band Pass Filter Unsharp mask

High Pass Filter

D t il f th O ti P f d th


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Details of the Operations Performed on the

Ima es Media Filter: Reduces noise in the active image by replacing each pixel with the median of

the neighboring pixel values

.

Mean Filter: Smooths the current image by replacing each pixel with the neighborhoodmean.

Minimum Filter: Filter does ra scale erosion b re lacin each ixel in the ima e with thesmallest pixel value in that pixels neighborhood.

Maximum Fitler: Filter does grayscale dilation by replacing each pixel in the image with thelar est ixel value in that ixels nei hborhood.

Gaussian Blur: Filter uses convolution with a Gaussian function for smoothing

onvo ve: oes spa a convo u on us ng a erne en ere

Unsharp Mask: Sharpens and enhances edges by subtracting a blurred version of the

image (the unsharp mask) from the original.

Variance Filter: Highlights edges in the image by replacing each pixel with theneighborhood variance.

Bandpass Filter: Removes high spatial frequencies (blurring the image) and low spatial

frequencies (similar to subtracting a blurred image). It can also suppress horizontal orvertical stripes that were created by scanning an image line by line.

C St dC St d 11


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Case StudyCase Study --11

InIn--service Ins ectionservice Ins ection Power Plant Life ExtensionPower Plant Life Extension

Sodium blockage in a hot argon communication

line of Fast Breeder Test Reactor at Kalpakkam.

Reactor shut down. Detection became critical.

Low densit material. Gamma source Ir 192.

Challenge Limited access, large area, low Z

. .

Approach - Advanced Image processing :

, ,

Sharpening - Edge Enhancement


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Case Study 2 Enhancement of Tenon Rivet Head-

Pow er Station in I ndia shutdown (as part of bi-annual maintenance and Turbine Generator w asopened for in-service inspect ion. During in- servicevisual inspect ion, the thickness of r ivet head of

t enon of blade 17 of HP rot or st age-2 w as found t obe less than other blade t enons and almost flushw it h shroud. Regulators w anted proof.

Low Cont rast radiograph by Gamm a Radiography

Approach Denoising+ Deblurring +

bandpass filter +contrast enhancement

Filtered and contrast enhanced image. Note that theedges of the rivet head can be seen as sharp points overthe shroud portion.

Contrast &Flow chart


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Contrast &

brightnessHistogram

Flow chartdevelopedfor Image

Equilisationprocessing

Edge Enhancement through Spatial filtering

Case Study 3 : Qualit y Assurance of

ASTM Standard + Image Enhancement =used in nuclear reactors

n ance n erpre a on an uan a veEvaluation forweld quality, sheath spacing, uniformity of

Materials Evaluation, May 2007

COMBINING ADVANCED CONCEPTS WITH

STANDARD PRACTICES FOR ROBUSTQUALITY ASSURANCE


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Basics of Radiography Testing and Image Processing

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