Wiki source code of 4. Image segmentation with ilastik
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1.3 | 1 | == Analysis approach for series of rodent brain section image == |
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1.1 | 2 | |
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1.3 | 3 | There are two main approaches for the analysis of rodent brain section images. |
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1.1 | 4 | |
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1.3 | 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//). | ||
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1.1 | 7 | |
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1.5 | 8 | **Which approach is best for my dataset?** |
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| 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. | ||
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1.6 | 12 | === Pixel classification workflow === |
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1.5 | 13 | |
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1.1 | 14 | |
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1.6 | 15 | ==== ==== |
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1.1 | 16 | |
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| 18 | Photo by David Clode | ||
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| 20 | ==== Or code ==== | ||
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| 22 | Code blocks can be added by using the code macro: | ||
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| 24 | {{code language="python"}} | ||
| 25 | x = 1 | ||
| 26 | if x == 1: | ||
| 27 | # indented four spaces | ||
| 28 | print("x is 1.") | ||
| 29 | {{/code}} | ||
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| 31 | (% class="wikigeneratedid" id="HH4Won27tAppearinToC" %) | ||
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