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A large number of datasets collected during several trials are processed by beam forming and scan conversion methods [32 35], generating the image sequences with a resolution grid of 0.05 × 0.05 m2, and two of them are selected for example in the following.
Since many modern television sets have an interlace to progressive scan conversion there is little difference in picture quality.
These data underwent a scan conversion using Matlab code provided by Siemens.
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In occasional fits of pique over particularly bad "pan and scan" conversions, I even shut off the TV.
Stent and lumen segmentation took ~46 seconds and scan-conversion to Cartesian images of 800·800 pixels ~24 seconds.
More specifically, the time required by the scan-conversion algorithm was ~1 second for an output image of pixel dimensions 2000 by 2000.
Computational times for scan-conversion, lumen segmentation, attenuation estimation and RF classification algorithms are in the order of seconds (<2 sec).
Algorithms for imaging catheter and guide-wire segmentation were implemented in Matlab 2011a, 64-bit version 7.12.0 (MathWorks, Natick, MA), while the algorithms for image scan-conversion [ 19, 20] were implemented in Visual C++ 2008 (Microsoft, Redmond, WA).
The scan-conversion algorithm was implemented in C++ (Microsoft, Redmond, WA) and the Random Forests classification algorithm is based on the original FORTRAN IBMM, Armonk, NY) code made available by the RF inventors [ 31].
In the first scan, the conversion reaction has anodic and cathodic peaks that are located at 1.85 and 1.2 V, respectively.
This uses the CT scan with conversion from Hounsfield units to attenuation coefficients for attenuation correction and for transmission-dependent scatter correction [10].
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