Attention: The EBRAINS drive will be unavailable for most of the weekend starting the 25th October. Although the Lab is availble while the Drive is down, files that are stored in the Drive will not be loaded and you will be unable to save documents directly on the Lab.


Last modified by annedevismes on 2021/06/08 11:56
From version 11.1
edited by evanhancock
on 2020/07/21 09:34
Change comment: There is no comment for this version
To version 13.2
edited by puchades
on 2020/09/24 12:14
Change comment: There is no comment for this version

Summary

Details

Page properties
Author
... ... @@ -1,1 +1,1 @@
1 -XWiki.evanhancock
1 +XWiki.puchades
Content
... ... @@ -11,7 +11,7 @@
11 11  
12 12  **Which approach is best for my dataset?**
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 based on object level features such as size and shape. The best approach is determined by trial and error.
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 based on object level features such as shape and size. The best approach is determined by trial and error.
15 15  
16 16  === (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
17 17  
... ... @@ -19,11 +19,31 @@
19 19  
20 20  **Basic steps:**
21 21  
22 -* Train the classifier with two classes (labeling and background)
22 +* Train the classifier with two classes (labelling and background)
23 23  * Apply the classifier to the rest of the images (batch processing)
24 24  * Export the probability maps in HDF5 format, and simple_segmentation images in PNG format with the default settings.
25 25  * Review the results.
26 26  
27 +[[image:Pixel_classification workflow.png||style="float:left"]]
28 +
29 +=== ===
30 +
31 +=== ===
32 +
33 +=== ===
34 +
35 +=== ===
36 +
37 +
38 +
39 +
40 +
41 +
42 +
43 +
44 +
45 +
46 +
27 27  === (% style="color:#c0392b" %)Object classification workflow(%%) ===
28 28  
29 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//**.**
... ... @@ -33,3 +33,5 @@
33 33  * Train the classifier with two classes (labelling and artefacts)
34 34  * In the **Object Information Export** applet, export “Object Predictions” in PNG format.  Do not change the default export location.
35 35  * Review the results.
56 +
57 +
Pixel_classification workflow.png
Author
... ... @@ -1,0 +1,1 @@
1 +XWiki.puchades
Size
... ... @@ -1,0 +1,1 @@
1 +1.0 MB
Content