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Last modified by annedevismes on 2021/06/08 11:56
From version 4.3
edited by puchades
on 2020/03/25 16:59
Change comment: There is no comment for this version
To version 6.1
edited by puchades
on 2020/03/26 10:48
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1 -== [[image:ilastik_logo.PNG||style="float:right"]]Analysis approach for series of rodent brain section image ==
1 +== [[image:ilastik_logo.PNG||style="float:right"]](% style="color:#c0392b" %)Analysis approach for series of rodent brain section image(%%) ==
2 2  
3 3  There are two main approaches for the analysis of rodent brain section images.
4 4  
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9 9  
10 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 -=== Pixel classification workflow ===
12 +=== (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
13 13  
14 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]]
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17 17  
18 18  -Train the classifier with two classes (labeling and background)
19 19  
20 --Apply the classifier to the rest of the images (Batch processing)
20 +-Apply the classifier to the rest of the images (batch processing)
21 21  
22 22  -Export the probability maps in HDH5 format, and simple_segmentation images in .png format, with the default settings.
23 23  
24 24  -Review our results.
25 25  
26 -=== Object classification workflow ===
26 +=== (% style="color:#c0392b" %)Object classification workflow(%%) ===
27 27  
28 28  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//**.**
29 29