Industrial radiography is a well established, reliable means of providing nondestructive structural integrity information. The majority of industrial radiographs are interpreted by trained human eyes using transmitted light and various visual aids. Hundreds of miles of radiographic information are evaluated, documented and archived annually. In many instances, there are serious considerations in terms of interpreter fatigue, subjectivity and limited archival space. Quite often it is difficult to quickly retrieve radiographic information for further analysis or investigation. Methods of improving the quality and efficiency of the radiographic process are being explored, developed and incorporated whenever feasible. High resolution cameras, digital image processing, and mass digital data storage offer interesting possibilities for improving the industrial radiographic process. A review is presented of computer aided radiographic interpretation technology in terms of how it could be used to enhance the radiographic interpretation process in evaluating radiographs of aluminum welds

Lloyd, J. F., Sr.

English

NASA Technical Reports Server

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Enclosure (11 to :  87M0-0987 
A REVIEW OF COMPUTER AIDED INTERPRETATION TECHNOLOGY FOR THE 
EVALUATION OF RADIOGRAPHS OF ALUMINUM WELDS 
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J. F. LLOYD, SR. QUALITY ENGINEER -7 
MARTIN MARIETTA MANNED SPACE SYSTEMS . . ,
ADVANCED QUALITY TECHNOLOGY 
NEW ORLEANS, LOUISIANA 
ABSTRACT: Industrial radiography is a well established, reliable 
means of providing nondestructive structural integrity information. 
The majority of industrial radiographs are interpreted by trained 
human eyes using transmitted light and various visual aids. 
Hundreds of miles of radiographic information are evaluated, 
documented and archived annually. In many instances, there are 
serious considerations in terms of interpreter fatigue, subjectivity 
and limited archival space. Quite often it is difficult to quickly 
retrieve radiographic information for further analysis or 
investigation. Methods of improving the quality and efficiency of 
the radiographic process are being explored, developed and 
incorporated whenever feasible. High resolution cameras, digital 
image processing, and mass digital data storage offer interesting 
possibilities for improving the industrial radiographic process. 
This paper will present a review of computer aided radiographic 
interpretation technology in terms of how it could be used to 
enhance the radiographic interpretation process in evaluating 
radiographs of aluminum welds. 
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INTRODUCTION: Radiographic film Interpretation, evaluation and 
archiving are time consuming, laborious and expensive operations. 
There is a definite need to reduce operator fatigue, 6UbJeCtiVity of 
interpretation, storage space and inspection time. In respon e to 
that need Martin M a r d t t a  Manned Space Systems has been surveying 
components for integration into a computer aided radiographic film 
interpretation system for use in evaluation of aluminum welds. 
Components being evaluated are: high resolution cameras, digital 
image processing, optical disk storage and voice recognition. 
1 
SURVEY RESULTS: 
CAMERAS: The results of an inspection process are only as 
good as the weakest link in the system. Of primary importance is 
the basic detector, the camera, which in effect must match or exceed 
the capability of the human eye. There are several cameras 
available in both the Vidicon and CCD categories which show 
acceptable image contrast and detail sensitivity detection 
capabilities. See figure 1 for contrast and detail sensitivity in 
radiograph of penetrameter. 
Figure 2 shows a Vidicon and a CCD camera. For this discussion 
Vidicon cameras have been generalized as cameras employing electron 
tube type sensors. Vidicons represent a mature technology with 
improvements coming at a slower rate. They are generally larger. 
CCD cameras have been generalized as cameras employing solid state 
image sensors. They represent a rapidly advancing technology. They 
are generally smaller and more rugged. Each has its own advantages 
and disadvantLges. 
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The Vidicon camera’s horizontal resolution is determined by 
the video amplifier’s bandwidth and the quality of construction of 
the vidicon itself. The vertical resolution is determined by the 
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number of horizontal scan lines. The video signal of the Vidicon 
camera is relatively noise free and follows the imaged object in a 
continuous fashion, thus the sampling analog-to-digital converter 
(A/D) can readily pick up a representative digital image. The 
vidicon though is susceptible to some extent to external magnetic 
fields, blooming and lag, and may suffer permanent damage from 
burning an image into its photosensitive surface. 
The CCD camera on the other hand is much more immune to image 
burning and blooming. Lag and magnetic fields are not problems. 
Its resolution is fixed by the geometry of the CCD chip array. The 
specification of this resolution and the actual number of photosites 
contributing to the picture sometimes appear in conflict. Care must 
be taken when interpreting this specification. The video signal of 
the CCD camera can contain a significant amount of clock noise in 
the t i m e  between actual pixel element signals. The sampling A I D  
must be synchronized to pixel rate and in the proper phase 
relationship or the clock noise will appear in the digitized image. 
Figure 3 shows a temporary setup of camera and radiograph 
viewer. Proper selection of a camera and lens will depend on 
application requirements. Camera costs can range from $2,080 to 
S90,000. CCD camera costs go to both price extremes while Vidicon 
prices fall into a smaller range near the low extreme. When 
balancing system requirements with equipment available cost is a 
sizable factor to be considered. We found that a vidicon with 1600 
TV lines and a CCD with 1024 x 512 elements were adequate for our 
requirements. 
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HONITORS: Selection of a monitor depends on application 
requirements and must complement the rest of the system. A high 
resolution black and white monitor is sufficient for viewing 
original or enhanced radiographic images since there is no color 
information in the original radiograph. However, a high resolution 
RGB color monitor allows pseudo coloring which can be used to 
sharply distinguish very slight intensity changes. It should be 
noted that room illumination and brightness and contrast settings 
have a great effect on the apparent quality of the monitor. 
IMAGE PROCESSING: Figure 4 shows an image processor used as 
part of a real time radiography system. Gray scale resolution and 
pixel resolution are basic consideration6 of an image processing 
system. Eight bits per pixel gray scale resolution is a reasonable 
choice for digitization and storage of images. While a higher 
number of bits appear better, the advantages would be in large part 
offset by increased storage requirements and increased noise pickup 
from the camera. There are dozens of image processing algorithms 
available to process and enhance data. ( Figure 5 shows a frame 
averaged image of a weld flaw.) Which ones to use depends on their 
particular effect on the information required. There are many 
usable filters which can improve the 'seeability' of flaw 
indications in radiographs of welds. Examples are: Laplacian, 
Sobel, Roberts, median, homomorphic filters, histogram equilization 
and neighborhood averaging. Other utilities such as zoom, scroll, 
pan, histograms, densitometric measuremente, and micrometer 
capabilities are also available. Additional information on typical 
image processing operations is given in Appendix 1. 
LASER DISK STORAGE: Since the digitization and storage of the 
resulting data amounts to many gigabytes of digital information, the 
archival mechanism should be compact and readily accessible. 
Figure 6 shows a representative laser optical disk storage 
subsystem. Laser optical disks have the capability of storing 
2-gigabytes of digital information (1-gigabyte per side). Other 
smaller versions are also available. Even though 2-gigabytes 
capacity appears to be large, it is quite likely that various forms 
of data compression would be needed to further increase storage 
density depending on the quantity of film to be evaluated and 
stored. Laser disk storage also offers the advantage of . 5  second 
recall time per frame of information. Film archival storage 
consumes a lot of space as shown by figure 7. Additional time is 
consumed in retrival by having to search serially through many feet 
of film to get to the section of interest. 
VOICE RECOGNITION: Initial surveys indicate that voice 
recognition could be used advantageously to command a considerable 
portion of system operation. Figure 8 shows a representative voice 
recognition subsystem. Verbally entered evaluations and other 
pertinent information could be archieved together with the actual 
digital information of the radiograph . More evaluation of 
real-world capabilities is necessary for a more definitive 
understanding of the voice recognition capabilities. 
COMPUTER AND PERIPHERALS: Figure 9 shows a laboratory setup 
of image processor, camera, film, and viewer. There are several 
compaters which would accomplish the requirements in a film 
interpretation system. The computer system arrangements for image 
processing can vary considerably. They can use general purpose 
micro, mini, or mainframe computers with image processing software 
or tkley can use special array and pipelined processor hardware under 
the orchestration of a general purpose computer. Others can be 
hardvired and hardcoded to perform a series of enhancement 
functions that a r e  very fast but inflexible. 
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CONCLUSIONS: A computer aided radiographic interpretation 
I system shows considerable promise as a viable tool to enhance the 
radiographic film intrepretatlon process for aluminum welds. The 
major components for such a system have been identified. They are 
high resolution cameras and monitors, image enhancement hardware and 
software, laser disk storage and voice recognition subsystems. 
While many of these components and systems have been evaluated, 
additional evaluation and data gathering are necessary. Toward this 
end, evaluation of a turn-key system on production film is planned 
for the near future. 
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LIST OF FIGURES: 
Fig. 1 
Fig. 2 
Fig. 3 
Fig. 4 
Fig. 5 
Fig. 6 
Fig. 7 
Fig. 8 
Fig. 9 
Detail and contrast sensitivity of penetrameter. 
Vidicon and CCD cameras. 
Camera and viewer. 
Advent I1 Image Processor. 
Weld flaw on monitor using frame averaging. 
Laser Optical Disk Storage literature. 
Film Cannister Archive. 
Voice Recognition literature. 
Image Processor, Camera, Film and Viewer. 
APPENDIX #1 Typical Image Processing Operations 
ORIGINAL PAGE 
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TYPICAL IMAGE PROCESSING OPERATIONS 
The following are simplified explanations of some of the digital 
imaging processing terms. 
CONVOLUTION 
Convolution is used for spatial filtering and finding image 
features. The convolution operation replaces a pixel’s value with 
the sum of that pixel’s value and its neighbors values, each 
weighted by a factor. The weighting factors are called the 
convolution kernel. These kernels can take on various sizes, 
however the typical kernels are square arrays such as 3x3, 5x5, 7x7, 
etc. It is not necessary for the arrays to be square but a square 
one can be reduced to a linear or rectangular array by making the 
weighting factors zero for the row(s) or column(s) which are not 
desired. The arrays are usually of odd dimensions so that there is 
not a shift of the image and also so that the effect of the kernel 
can be syrnetric if desired. Convolution is computationally 
expensive, therefore, larger kernels significantly slow down image 
processing. Convolution ~esult in a border of garbage around the 
image edge where the kernel cannot be properly applied to the edge 
pixels. The larger the kernels are and the number of times the 
convolution is done results in larger borders. 
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DIFFERENTIATION 
Differentiation can be used to sharpen the image. Differentiation 
quantifies the rate of change of the intensities of the image. 
Edges usually have significant intensity changes and they can be 
enhanced through differentation. In the continuous realm a gradient 
vector at any point is formed by taking the partial derivatives in x 
and y at that point. Since we are dealing with digital images there 
are a finite number of points. The magnitudes of the gradient 
vector at each of these points is approximated by sums of 
differences . These gradient magnitudes are generated by applying 
various difference operators to the digitized image at each point. 
These operators are small orthorgonal arrays. The common 
approximation of the gradient g(x,y) is derived from the image 
f(x,y) by taking the sum of the absolute values of the differences 
of the intensities between the current x and y position and the next 
position in the x direction (x+l) and the next position in the y 
direction (y+l). Mathematically 
The difference operators in this case are: 
1 -1 1 0 
0 0 -1 0 
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ROBERTS FILTER 
The Roberts Edge Filter or Roberts Gradient is a variation of the 
gradient as described above. Mathematically 
g ( x ,  y )  = abslf ( x ,  y )  -f (x+l, y + l )  1 + absCf (x+l, y 1 -f ( x ,  y + l  ) 1. 
The difference operators in the case are: 
1 0 and 0 1 
0 -1 -1 0 
PREWITT and SOBEL 
Prewitt and Sobel Edge operators are 3x3. There effect is to reduce 
the effects of noise by local averaging. The Sobel operators give 
larger weighting to the closer pixels than to the corner pixels. 
Prewitt operators: 
-1 0 1 
-1 0 1 
-1 0 1 
Sobel operators: 
-1 0 1 
-2 0 2 
-1 0 1 
and 
and 
1 1 1 
0 0 0 
-1 -1 -1 
1 2 1 
0 0 0 
-1 -2 -1 
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LAPLACIAN 
The Laplacian is an edge detection operator. It is an approximation 
to the mathematical Laplacian, that is the second partial derivative 
of f(x,y) with respect to x and y. 
The Laplacian has two disadvantages: 
e.) directional information is not available, and 
b. 1 it enhances noise. 
One version of the discrete Laplacian is given by 
This edge detection operator in kernel form is 
0 -1 0 
-1 4 -1 
0 -1 0 
' *  , ' .. .__ ;; ' . ..*,;, 
.. ._ 
Another Laplacian edge detection kernel is 
-1 -1 -1 
-1 a -1 
-1 -1 -1 
Laplacian edge enhancement kernels have additional weighting at the 
center which retains the original image but  enhances the edges. 
Here are two of these kernels: 
0 -1 0 -1 -1 -1 
-1 5 -1 and -1 9 -1 
0 -1 0 -1 -1 -1 
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f(x,y) - In 
HOMOMORPHIC FILTERING 
r - FFT -H(u, v )  Inv FFT - exp - g(x, y )  
An image can be approximated by an illumination-reflectance mocel. 
The illumination is responsible for the dynamic range and in general 
is characterised by slow spatial variations. On the other hand 
contrast is a function of the reflectivity of objects and varies 
abruptly particularly at object boundaries. A homomorphic filter 
vhich will affect low and high spatial frequencies differently can 
effectivey compress the dynamic range and enhance the contrast 
simultaneously if the low frequencies are attenuated and the high 
frequencies amplified. This process is mathematically involved. 
TIME AVERAGING 
Averaging multiple noisy images will remove a large portion of the 
uncorrelated noise. The improvement will depend on the number of 
images averaged. This is useful for image acquisition where the 
original image is time invariant, such as is the case in digitizing 
radiographic images. 
NEIGHBORHOOD AVERAGING 
A pixel is replaced by a weighted average of its neighbors. This 
tends to remove noise, however, it also removes detail and causes 
edge blurring. Some averaging kernels are: 
0 1 0 1 1 1 
1/5 1 1 1 ? 119 1 1 1 ,  
0 1 0 1 1 1 
1 1 1 1 2 1 
1/10 1 2 1 ? and 1/16 2 4 2 
1 1 1 1 2 1 .  
MEDIAN FILTERING 
A pixel is replaced by the middle value of its neighbors after they 
are numerically sorted. 


A review of computer aided interpretation technology for the evaluation of radiographs of aluminum welds

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