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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		-=== Object classification workflow ===
	16	+==== Or code ====
17	17
	18	+Code blocks can be added by using the code macro:
18	18
19		-==== ====
	20	+{{code language="python"}}
	21	+x = 1
	22	+if x == 1:
	23	+ # indented four spaces
	24	+ print("x is 1.")
	25	+{{/code}}
20	20
21		-
	27	+(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
22	22