Aims and objectives
The Penumbra Effect describes blurring at the margins of an image profile due to the finite size of the energy source.
A computer aided detection (CAD) algorithm based on the De-Convolution Technique  can be used to pinpoint true-edge positions to sub-pixel accuracy and remove the Penumbra Effect via sub-pixel transfers without trade-off losses.
Enhancement is achieved by the removal of the Penumbra Effect.
A Pixelization Effect is also overcome at the same time.
We hypothesize that...
Methods and materials
Composite phantom and New York Catphan 500 images were used for calibration.
Nine CT images and Seven MR images were used for analyzes and distance,
and volume of effects measured.
Phantom calibrations: Measurements: area/volume at 1% low-contrast,
accurate to less than 1/10th of a pixel. Field Examples: (1) CT of a 68 year-old male patient's liver tumor.
Figure 1 shows the original image of Slice_00009198#18 with ROI (region of interest) placed on the tumor area.
Figures 2a and 2b show the 'before' (left) and 'after' (right) enhancement of 7xROI.
tumor measurements (in Figure 3) were: area,
A=610 ± 6.14mm 2 and volume,
V=3050.64 ± 30.70mm 3 .
Enhanced images enable clearer diagnosis and accurate measurements of distance,
and volume for treatment monitoring.
For further inquiries,
please contact Dr.
Kui Ming Chui,
either via website: www.iet.org.uk or via e-mail: email@example.com.
Reference:  “A De-Convolution Technique used for NDT in X-ray and CT”,
Oral/Full paper presentation in 17th World Conference on Non-Destructive Testing (17WCNDT),
2008 (NDT.net – The e-Journal & Database of Non-Destructive Testing – ISSN 1435-4934).