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1		-== Analysis approach for series of rodent brain section image ==
	1	+== H2 Headings Will Appear in Table of Content ==
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3		-There are two main approaches for the analysis of rodent brain section images.
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5		-1. Pixel classification only (with two or more classes)
6		-1. Pixel classification with two classes (//immunoreactivity// and //background//), followed by object classification with two classes (//objects-of-interest// and //artefact//).
	4	+Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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8		-**Which approach is best for my dataset?**
	6	+>This is a quote. You can add a quote by selecting some text and clicking the quote button in the editor.
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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.
	8	+Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
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12		-=== Pixel classification workflow ===
	10	+=== H3 Headings Will Appear In The Table of Content ===
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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]]
	12	+==== You can also add images ====
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16		-**Basic steps:**
	14	+[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
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18		--Train the classifier with two classes (labeling and background)
	16	+Photo by David Clode
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20		--Apply the classifier to the rest of the images (Batch processing)
	18	+==== Or code ====
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22		--Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
	20	+Code blocks can be added by using the code macro:
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24		--Review our results.
	22	+{{code language="python"}}
	23	+x = 1
	24	+if x == 1:
	25	+ # indented four spaces
	26	+ print("x is 1.")
	27	+{{/code}}
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26		-=== Object classification workflow ===
27		-
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//**.**
29		-
30		--Save the object classification file in the same folder as the raw images for analysis.  If the images are moved after the ilastik file is created, the link between the ilastik file and the images may be lost, resulting in a corrupted file.
31		-
32		--In the **Input Data** applet, upload the original images and their respective probability maps in HDH5 format (output from the pixel classification).
33		-
34		--Train the classifier with two classes (labeling and artefacts)
35		-
36		-(% class="wikigeneratedid" id="H" %)
37		--In the **Object Information Export** applet, export “Object Predictions” in PNG format.  Do not change the default export location
38		-
39		-
	29	+(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
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