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1		-== [[image:ilastik_logo.PNG||style="float:right"]]Analysis approach for series of rodent brain section image ==
	1	+== [[image:ilastik_logo.PNG||style="float:right"]](% style="color:#c0392b" %)Analysis approach for series of rodent brain section image(%%) ==
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3	3	There are two main approaches for the analysis of rodent brain section images.
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10	10	As a general rule, pixel classification is suitable for images in which there are clear differences in the colour, intensity and/ or texture of the feature-of-interest versus the background and other structures.  If there is non-specific labelling in the image that is very similar in appearance to the labelling-of-interest, object classification may allow the non-specific labelling to be filtered out based on object level features such as size and shape. The best approach is determined by trial and error.
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12		-=== Pixel classification workflow ===
	12	+=== (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
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14	14	For a quick introduction, watch: [[https:~~/~~/www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be>>url:https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]]
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18	18	-Train the classifier with two classes (labeling and background)
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20		--Apply the classifier to the rest of the images (Batch processing)
	20	+-Apply the classifier to the rest of the images (batch processing)
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22	22	-Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
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24	24	-Review our results.
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26		-=== Object classification workflow ===
	26	+=== (% style="color:#c0392b" %)Object classification workflow(%%) ===
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28	28	There are three options on the ilastik start up page for running Object Classification.  Choose the //Object Classification with Raw Data and Pixel Prediction Maps as input//**.**
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