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Last modified by annedevismes on 2021/06/08 11:56
From version 20.3
edited by annedevismes
on 2021/06/08 11:56
Change comment: There is no comment for this version
To version 1.3
edited by puchades
on 2020/03/25 14:37
Change comment: There is no comment for this version

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1 -== [[image:ilastik_logo.PNG||style="float:right"]] ==
1 +== Analysis approach for series of rodent brain section image ==
2 2  
3 -== [[image:Pixel_classification workflow.png||style="float:left"]](% style="color:#c0392b" %)Analysis approach for series of rodent-brain section image(%%) ==
3 +There are two main approaches for the analysis of rodent brain section images.
4 4  
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.
5 +1. Pixel classification only (with two or more classes)
6 +1. Pixel classification with two classes (//immunoreactivity// and //background//), followed by object classification with two classes (//objects-of-interest// and //artefact//).
6 6  
7 -**There are two main approaches for the analysis of rodent-brain section image~:**
8 +=== H3 Headings Will Appear In The Table of Content ===
8 8  
9 -1. pixel classification only (with two or more classes) and
10 -1. pixel classification with two classes (//immunoreactivity// and //background//), followed by Object classification with two classes (//objects of interest// and //artefact//).
10 +==== You can also add images ====
11 11  
12 -**Which approach is best for my dataset?**
12 +[[image:Collaboratory.Apps.Article.Code.ArticleSheet@placeholder.jpg]]
13 13  
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 on the basis of the object-level features such as shape and size. The best approach is determined by trial and error.
14 +Photo by David Clode
15 15  
16 -=== (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
16 +==== Or code ====
17 17  
18 -For a quick introduction, [[watch this video>>https://www.youtube.com/watch?v=5N0XYW9gRZY&feature=youtu.be]].
18 +Code blocks can be added by using the code macro:
19 19  
20 -**Basic steps**
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 -* 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.
26 -
27 -=== (% style="color:#c0392b" %)Object-classification workflow(%%) ===
28 -
29 -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.
30 -
31 -* 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.
32 -* In the "Input Data" applet, upload the original images and their respective probability maps in HDF5 format (output from the Pixel Classification).
33 -* Train the classifier with two classes (labelling and artefacts).
34 -* In the "Object Information Export" applet, export “Object Predictions” in PNG format.  Do not change the default export location.
35 -* Review the results.
27 +(% class="wikigeneratedid" id="HH4Won27tAppearinToC" %)
28 +
Pixel_classification workflow.png
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