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Merge pull request #79 from jhlegarreta/FixSegmentWithWatershedImageFilterDocError
DOC: Fix `SegmentWithWatershedImageFilter` example doc error.
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src/Segmentation/Watersheds/SegmentWithWatershedImageFilter/Code.cxx

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#include "itkGradientMagnitudeImageFilter.h"
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// Run with:
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// ./WatershedImageFilter threshold level
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// ./SegmentWithWatershedImageFilter inputImageFile outputImageFile threshold level
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// e.g.
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// ./WatershedImageFilter 0.005 .5
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// ./SegmentWithWatershedImageFilter BrainProtonDensitySlice.png OutBrainWatershed.png 0.005 .5
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// (A rule of thumb is to set the Threshold to be about 1 / 100 of the Level.)
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int main( int argc, char *argv[] )

src/Segmentation/Watersheds/SegmentWithWatershedImageFilter/Code.py

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# Run with:
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# ./WatershedImageFilter.py <InputFileName> <OutputFileName> <Threshold> <Level>
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# e.g.
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# ./WatershedImageFilter.py BrainProtonDensitySlice.png OutBrainWatersehd.png 0.005 .5
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# ./WatershedImageFilter.py BrainProtonDensitySlice.png OutBrainWatershed.png 0.005 .5
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# (A rule of thumb is to set the Threshold to be about 1 / 100 of the Level.)
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#
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# threshold: is used to set the absolute minimum height value used during processing.
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# threshold: absolute minimum height value used during processing.
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# Raising this threshold percentage effectively decreases the number of local minima in the input,
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# resulting in an initial segmentation with fewer regions.
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# The assumption is that the shallow regions that thresholding removes are of of less interest.
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# A level of 1.0 is analogous to flooding the image up to a
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# depth that is 100 percent of the maximum value in the image.
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# A level of 0.0 produces the basic segmentation, which will typically be very oversegmented.
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# Level values of interest are typically low (i.e. less than about 0.40 or 40% ),
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# Level values of interest are typically low (i.e. less than about 0.40 or 40%),
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# since higher values quickly start to undersegment the image.
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import sys

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