Influence of the Processes Parameters on the Properties of The ...
Influence of the Processes Parameters on the Properties of The ...
Influence of the Processes Parameters on the Properties of The ...
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Chapter 3.<br />
Analytical Methods and Designs <str<strong>on</strong>g>of</str<strong>on</strong>g> Experiments<br />
Figure 3.14: Schematic representati<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> interacti<strong>on</strong>s beam <strong>on</strong> specimen surface.<br />
5.2.1 Bases <str<strong>on</strong>g>of</str<strong>on</strong>g> Image Analysis<br />
SCION ® Image was originally developed for <str<strong>on</strong>g>the</str<strong>on</strong>g> Nati<strong>on</strong>al Institutes <str<strong>on</strong>g>of</str<strong>on</strong>g> Health, a federal<br />
government agency. Basic image properties like c<strong>on</strong>trast, brightness and gamma can be optimized. For<br />
particle analysis <str<strong>on</strong>g>the</str<strong>on</strong>g> most important property is <str<strong>on</strong>g>the</str<strong>on</strong>g> grey level <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> image which can be segmented to<br />
reduce different shades [Niemistö, 2006].<br />
Segmentati<strong>on</strong> means <str<strong>on</strong>g>the</str<strong>on</strong>g> separati<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> different parts <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> image; a foreground objects like<br />
particles from a background <str<strong>on</strong>g>of</str<strong>on</strong>g> image. In <str<strong>on</strong>g>the</str<strong>on</strong>g> pore size and shape analysis segmentati<strong>on</strong> has to be d<strong>on</strong>e with<br />
very high accuracy because <str<strong>on</strong>g>the</str<strong>on</strong>g> area <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> particle is dependent <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> accuracy <str<strong>on</strong>g>of</str<strong>on</strong>g> segmentati<strong>on</strong> and <str<strong>on</strong>g>the</str<strong>on</strong>g><br />
results <str<strong>on</strong>g>of</str<strong>on</strong>g> analysis have to be reliable [Niemistö, 2006]. Segmentati<strong>on</strong> can base <strong>on</strong> ei<str<strong>on</strong>g>the</str<strong>on</strong>g>r disc<strong>on</strong>tinuity or<br />
similarity <str<strong>on</strong>g>of</str<strong>on</strong>g> intensity values. Disc<strong>on</strong>tinuity methods find abrupt changes in <str<strong>on</strong>g>the</str<strong>on</strong>g> intensity and separate<br />
various regi<strong>on</strong>s <strong>on</strong> that way. Methods <str<strong>on</strong>g>of</str<strong>on</strong>g> similarity needs predefined criteria <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> intensity value and<br />
separate regi<strong>on</strong>s based <strong>on</strong> that.<br />
Thresholding, clustering, regi<strong>on</strong> growing, regi<strong>on</strong> merging and regi<strong>on</strong> splitting are methods which<br />
are included in <str<strong>on</strong>g>the</str<strong>on</strong>g> category <str<strong>on</strong>g>of</str<strong>on</strong>g> similarity methods [Niemistö, 2006]. Thresholding is a central method <str<strong>on</strong>g>of</str<strong>on</strong>g><br />
segmentati<strong>on</strong> due to its simple and intuitive properties. It separates bright foreground objects <strong>on</strong> a dark<br />
background and can be defined as:<br />
(3.13)<br />
where f(x) = grey level <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> point x, f T (x) = <str<strong>on</strong>g>the</str<strong>on</strong>g> respective point in <str<strong>on</strong>g>the</str<strong>on</strong>g> thresholded image and T<br />
is <str<strong>on</strong>g>the</str<strong>on</strong>g> threshold.<br />
If a pixel in f T gets value 1, it is called a foreground (or object point) and if it gets value 0, it is<br />
called a background. Threshold T can be <str<strong>on</strong>g>the</str<strong>on</strong>g> same for <str<strong>on</strong>g>the</str<strong>on</strong>g> whole image (global threshold) or <str<strong>on</strong>g>the</str<strong>on</strong>g>re can be<br />
different thresholds in different parts <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> image (local threshold) [Niemistö, 2006]. <strong>The</strong> transiti<strong>on</strong> between<br />
<str<strong>on</strong>g>the</str<strong>on</strong>g> object point and <str<strong>on</strong>g>the</str<strong>on</strong>g> background may be so unsteady that a human can’t decide where <str<strong>on</strong>g>the</str<strong>on</strong>g> borders<br />
between <str<strong>on</strong>g>the</str<strong>on</strong>g> object and <str<strong>on</strong>g>the</str<strong>on</strong>g> background exactly go.<br />
Many papers are published <strong>on</strong> <str<strong>on</strong>g>the</str<strong>on</strong>g> automatic selecti<strong>on</strong> <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> threshold since 1960’s. <strong>The</strong> most<br />
comm<strong>on</strong>ly used method is created by Otsu [1979]. That method maximizes <str<strong>on</strong>g>the</str<strong>on</strong>g> class variance <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> grey<br />
levels between <str<strong>on</strong>g>the</str<strong>on</strong>g> objects and <str<strong>on</strong>g>the</str<strong>on</strong>g> background and minimizes <str<strong>on</strong>g>the</str<strong>on</strong>g> intra-class variance. Usually threshold is<br />
selected from a histogram <str<strong>on</strong>g>of</str<strong>on</strong>g> <str<strong>on</strong>g>the</str<strong>on</strong>g> image. If <str<strong>on</strong>g>the</str<strong>on</strong>g> histogram is bimodal threshold should be selected between <str<strong>on</strong>g>the</str<strong>on</strong>g><br />
modes because supposedly a <strong>on</strong>e mode represents <str<strong>on</strong>g>the</str<strong>on</strong>g> foreground and <str<strong>on</strong>g>the</str<strong>on</strong>g> o<str<strong>on</strong>g>the</str<strong>on</strong>g>r <strong>on</strong>e represents <str<strong>on</strong>g>the</str<strong>on</strong>g><br />
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