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5	5	1. Pixel classification only (with two or more classes)
6	6	1. Pixel classification with two classes (//immunoreactivity// and //background//), followed by object classification with two classes (//objects-of-interest// and //artefact//).
7	7
8		-**Which approach is best for my dataset?**
	8	+=== H3 Headings Will Appear In The Table of Content ===
9	9
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.
	10	+==== You can also add images ====
11	11
12		-=== Pixel classification workflow ===
	12	+[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
13	13
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]]
	14	+Photo by David Clode
15	15
16		-**Basic steps:**
	16	+==== Or code ====
17	17
18		--Train the classifier with two classes (labeling and background)
	18	+Code blocks can be added by using the code macro:
19	19
20		--Apply the classifier to the rest of the images (Batch processing)
	20	+{{code language="python"}}
	21	+x = 1
	22	+if x == 1:
	23	+ # indented four spaces
	24	+ print("x is 1.")
	25	+{{/code}}
21	21
22		--Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
23		-
24		-=== Object classification workflow ===
25		-
26		-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//**.**
27		-
28		--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.
29		-
30		--
31		-
32		-==== ====
33		-
34		-
	27	+(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
35	35