Last modified by annedevismes on 2021/06/08 11:56
From version 1.6
edited by puchades
on 2020/03/25 14:47
Change comment: There is no comment for this version
To version 1.4
edited by puchades
on 2020/03/25 14:41
Change comment: There is no comment for this version

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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 -
15 -==== ====
16 -
17 -
18 18  Photo by David Clode
19 19  
20 20  ==== Or code ====