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The development & integration of a Real-Time X-Ray image transfer system over a wireless network
Final Year Project Presentation
Capt David Clarke
Outline Of Presentation
• Background to Project• Technology Research• Project Overview• Image Compression• Matlab• MSE• Gumstix• Main tasks Completed
Background to Project
• The Army Ordnance Corps recently upgraded their HOBO robot
• Using COFDM for video link• Also use a manual x-ray system• Reamda Ltd investigating use of
COFDM or ethernet
to send x-ray picture wirelessly• Intention was to investigate the sending of a
compressed image• Investigate image comparison
Technology Research
• COFDM – Coded Orthogonal Frequency Division Multiplexing
• Non-Destructive Testing [Digital X-Ray]• TIF and JPEG• Gumstix and Linux
COFDM
• Multi-carrier transmission technique• Uses bandwidth efficiently• Very robust against channel interference• Uses a Guard Interval
Non-Destructive Testing
• Involves Digital Radiography• Gives Instant read-out of an x-ray image• Currently it is Manually Deployed• Images are quite large • Must be compressed in order to transmit
• Non-Destructive testing is used in many industries
JPEG
• Standardised image compression algorithm• Lossy image• Trade-off of file size vs image quality• Mathematical formula• Source code freely available• Many myths about JPEG
Project Overview
• Research• Sample x-ray images compressed
from TIF to JPEG• Image Quality assessed by experts
and using Matlab• MSE method investigated• Gumstix used to send new images
Assess the Image Quality
Use Gumstix as remote computer
to send compressed pics
Research of Current
Technology
Investigate image Compression from
TIF to JPEG
Suggestions for overall system
Image Compression
• Simply done using ImageConverter PLUS• TIF files of 13 MB reduced to100 – 300KB
when converted to JPEG• On using Gumstix, it was found that zipping the files reduced
size to approx 400 – 600KB
100% Quality 85% Quality
Image Compression Assessed
• Compressed Images sent to Ordnance Workshops and assessed by experts
• All 6 images sent were approved for use (100% Quality)• 5 out of 6 images were approved for use (85% Quality)• The images were scored out of ten – Mean and SD calculated• JPEG Myth ?
Matlab• Matlab used to find the MSE [Mean Square Error] of before and
after images• Using MSE, PSNR [Peak Signal-to-Noise Ratio] was then
calculated
PSNR = 10*log10((MAX(L))^2 / MSE))• Plotted against Compression rate
MSE• Mean Square Error has many good attributes
• Simple to use – Natural to define energy of a signal – Possesses symmetry and differentiability - Widely used in signal processing
• However, using it to predict human perception of image quality is not deemed a good idea
• Too many assumptions – Random re-ordering
- MSE wont pick up an error
- Signal fidelity as important as signal error samples
Gumstix
• Connex 400xm• Used to send the image to the host PC• Linux had to be learned • http://ohm.nuigalway.ie/wiki • http://docwiki.gumstix.org• Simple commands used to zip image and scp to host pc
Main Tasks Completed
• Research of Technology• Knowledge gained of Army Equipment• Image Compression and Comparison• Matlab• Understanding of Linux• First time to use Gumstix