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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		-=== H3 Headings Will Appear In The Table of Content ===
	8	+**Which approach is best for my dataset?**
9	9
10		-==== You can also add images ====
	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.
11	11
12		-[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
	12	+=== Pixel classification workflow ===
13	13
14		-Photo by David Clode
	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]]
15	15
16		-==== Or code ====
	16	+=== Object classification workflow ===
17	17
18		-Code blocks can be added by using the code macro:
19	19
20		-{{code language="python"}}
21		-x = 1
22		-if x == 1:
23		- # indented four spaces
24		- print("x is 1.")
25		-{{/code}}
	19	+==== ====
26	26
27		-(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
	21	+
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