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 CATS is a big image data compatible [Fiji](http://fiji.sc/) plugin for trainable image segmentation. 
 The code is partly based on Fiji's [Trainable Weka Segmentation](https://github.com/fiji/Trainable_Segmentation) (TWS) plugin.
 
-Similar to Fiji's [Trainable Weka Segmentation](https://github.com/fiji/Trainable_Segmentation) and the stand-alone software [ilastik](https://www.ilastik.org/), CATS learns an image segmentation from user drawn annotations by computing image features and feeding them into a [Random Forest](https://en.wikipedia.org/wiki/Random_forest) classifier. Again similar to TWS and ilastik, the image features of CATS are based on the Eigenvalues of the [Hessian matrix](https://en.wikipedia.org/wiki/Hessian_matrix) and the [Structure Tensor](https://en.wikipedia.org/wiki/Structure_tensor), which provide rotationally invariant information about edges and ridges in an image's gray-value distribution. 
+Similar to Fiji's TWS and the stand-alone software [ilastik](https://www.ilastik.org/), CATS learns an image segmentation from user drawn annotations by computing image features and feeding them into a [Random Forest](https://en.wikipedia.org/wiki/Random_forest) classifier. Again similar to TWS and ilastik, the image features of CATS are based on the Eigenvalues of the [Hessian matrix](https://en.wikipedia.org/wiki/Hessian_matrix) and the [Structure Tensor](https://en.wikipedia.org/wiki/Structure_tensor), which provide rotationally invariant information about edges and ridges in an image's gray-value distribution. 
 
 In order to improve context learning, CATS however also computes "Deep Eigenvalue Features", such as, e.g.: