| layout | title |
|---|---|
ilastik06 |
ilastik 0.6 - Overview |
ilastik is a simple, user-friendly tool for image classification and segmentation in up to three spatial and one spectral dimension. Using it requires no experience in image processing.
ilastik has a convenient mouse interface for labeling an arbitrary number of classes in the images. These labels, along with a set of generic (nonlinear) image features, are then used to train a Random Forest classifier. In the interactive training mode, ilastik provides real-time feedback of the current classifier predictions and thus allows for targeted training and overall reduced labeling time. In addition, an uncertainty measure can guide the user to ambiguous regions of the data. Once the classifier has been trained on a representative subset of the data, it can be exported and used to automatically process a very large number of images.
The features are computed in the full 2D/3D/4D pixel neighborhoods, depending on the available data. While the provided set of features includes popular color, edge and texture descriptors, the plug-in functionality allows advanced users to add their own problem-specific features. Feature computation and classifier prediction are multi-threaded and fully exploit modern multi-core machines.
So far, we have used ilastik successfully on applications from the neurosciences (segmentation of EM images), systems biology (high throughput screening experiments) and industrial quality control.