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