Last modified by annedevismes on 2021/06/08 11:56
From version 12.1
edited by evanhancock
on 2020/07/21 09:38
Change comment: There is no comment for this version
To version 11.1
edited by evanhancock
on 2020/07/21 09:34
Change comment: There is no comment for this version

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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 shape and size. 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 size and shape. The best approach is determined by trial and error.
15 15  
16 16  === (% style="color:#c0392b" %)Pixel classification workflow(%%) ===
17 17  
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19 19  
20 20  **Basic steps:**
21 21  
22 -* Train the classifier with two classes (labelling and background)
22 +* Train the classifier with two classes (labeling 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.