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Sholl Analysis

1 byte removed, 13:13, 1 April 2016
Fix layout for wider skin
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==== Cf. Segmentation ====
Press ''More» Cf. Segmentation'' to visually confirm which phase of the segmented image will be sampled. This command highlights foreground from background pixels and is particularly useful when analyzing black and white (binary) images or when using the ''B&W'' lookup table in the Threshold Widget (<span style="border-bottom:1px dotted #ccc;">Image▷ Adjust▷ Threshold...</span> {{key press|Shift|T}}). ''Cf. Segmentation'' allows you to ensure that you are measuring neuronal processes and not the interstitial spaces between them. Here is an example using an axonal arbor of a Drosophila olfactory neuron from the [http://diademchallenge.org DIADEM] dataset<ref name="DIADEM"/>:
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|<center><span style="display:inline-block;text-align:center;width:310px">Segmented image</span> <span style="display:inline-block;text-align:center;width:160px">''Cf. Segmentation output''</span> <span style="display:inline-block;text-align:center;width:270px">''Intersections mask''</span></center>
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|<center>[[File:CfSegmentation.png|700px|center|]]</center>
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|<center>'''Top row:''' Image properly segmented: Arbor is sampled. '''Bottom row:''' Aberrant segmentation (inverted image): Background is sampled.<br/center>Note the reversal of ''[[#Cf._Segmentation|Cf. Segmentation]] output'' and how the ''[[#Output_Options|intersections mask]]'' no longer decorates the axonal processes but the interstitial spaces between them. The consequences of the phase inversion are twofold: 1) the program oversamples (cf. hue ramps on upper left of ''Intersections mask'') and 2) the program detects artifacts induced by the edges of the image (cf. top-right and bottom-right corners of mask where intersections are sampled in the absence of real axons at those locations). Also, note that the initial black and white image would ''look the same'' under an inverted lookup table (<span style{{bc|color="border-bottom:1px dotted #ccc;">Image▷ white|Image|Lookup Tables▷ Tables|Invert LUT</span>}}).
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{{Tip
| tip = With binary images, ''Sholl Analysis'' treats zero intensities as the background, independently of the image lookup table or the state of the ''Black background option'' in <span style="border-bottom:1px dotted #ccc;">Process▷ Binary▷ Options...</span> As with any other [http://imagej.net/docs/guide/146-29.html#infobox:blackBackground ImageJ routine], confusing background with foreground pixels will lead to aberrant results, including: 1) overestimation of branches and 2) artifacts at distances intersecting the boundaries of the image canvas.
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== Sholl Plots ==
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