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  • Pixel_classification workflow.png
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1		-== [[image:ilastik_logo.PNG||style="float:right"]] ==
	1	+== H2 Headings Will Appear in Table of Content ==
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3		-== (% style="color:#c0392b" %)Analysis approach for series of rodent brain section image(%%) ==
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5		-Ilastik is a versatile image analysis tool specifically designed for the classification, segmentation and analysis of biological images based on supervised machine learning algorithms.
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7		-There are two main approaches for the analysis of rodent brain section images.
	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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9		-1. Pixel classification only (with two or more classes)
10		-1. Pixel classification with two classes (//immunoreactivity// and //background//), followed by object classification with two classes (//objects of interest// and //artefact//).
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12		-**Which approach is best for my dataset?**
	10	+=== H3 Headings Will Appear In The Table of Content ===
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14		-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 (labelling) 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 shape and size. The best approach is determined by trial and error.
	12	+==== You can also add images ====
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16		-=== (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
	14	+[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
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18		-For a quick introduction, [[watch this video>>https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]].
	16	+Photo by David Clode
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20		-**Basic steps:**
	18	+==== Or code ====
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22		-* Train the classifier with two classes (labelling and background)
23		-* Apply the classifier to the rest of the images (batch processing)
24		-* Export the probability maps in HDF5 format, and simple_segmentation images in PNG format with the default settings.
25		-* Review the results.
	20	+Code blocks can be added by using the code macro:
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27		-[[image:Pixel_classification workflow.png||style="float:left"]]
	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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47		-=== (% style="color:#c0392b" %)Object classification workflow(%%) ===
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49		-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//**.**
50		-
51		-* 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.
52		-* In the **Input Data** applet, upload the original images and their respective probability maps in HDF5 format (output from the Pixel Classification).
53		-* Train the classifier with two classes (labelling and artefacts)
54		-* In the **Object Information Export** applet, export “Object Predictions” in PNG format.  Do not change the default export location.
55		-* Review the results.
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Pixel_classification workflow.png

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