I used the pixel classification for fluorescence signal detection and it works great.
Now, I want to dig a little deeper to understand how (or why) it worked, not from the technical aspect, but from feature selection aspect, i.e. which features are used for the random forest classifier & what do they mean.
This is the result from
Feature Selection with
auto, penalty 0.05
Gaussian Smoothing (σ=5.0) in 2D Gaussian Gradient Magnitude (σ=10.0) in 2D Difference of Gaussians (σ=10.0) in 2D Structure Tensor Eigenvalues (σ=0.7) in 2D  Structure Tensor Eigenvalues (σ=3.5) in 2D  Hessian of Gaussian Eigenvalues (σ=1.0) in 2D 
I have some troubles interpreting this result.
This is what I would say:
the Gaussian smoothing, Gaussian gradient magnitude and DoG contribute to the classifier on images blurred with large Gaussian kernel (σ ~ 10.0)
As for the rest of the parameters, they contribute more on images blurred with small Gaussian kernel (to detect finer structure ??)
I’m not someone with very solid math background, so correct me if I get things wrong.