Wiki source code of 4. Image segmentation with ilastik
Show last authors
| author | version | line-number | content |
|---|---|---|---|
| 1 | == Analysis approach for series of rodent brain section image == | ||
| 2 | |||
| 3 | There are two main approaches for the analysis of rodent brain section images. | ||
| 4 | |||
| 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//). | ||
| 7 | |||
| 8 | **Which approach is best for my dataset?** | ||
| 9 | |||
| 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 | |||
| 12 | === Pixel classification workflow === | ||
| 13 | |||
| 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]] | ||
| 15 | |||
| 16 | === Object classification workflow === | ||
| 17 | |||
| 18 | |||
| 19 | ==== ==== | ||
| 20 | |||
| 21 | |||
| 22 |