Working. Please wait....

Feature Extraction

The first step of the pipeline is the Feature Extraction. In this example, two different images are used for trainging and test set. So two feature extraction have to be performed. The train input file is an image of the Lupus III region (image A), while the test input file is an image of the Lupus I region (image B). All the kinds of features are calculated with parameteres set in the following form.

Train image file:
Test image file:
Add white noise?
Compute Haar-like Features?
Min. Window resolution (pixels):
Max. Window resolution (pixels):
Compute Haralick Features?
Min. Co-occurrence matrix Distance :
Max. Co-occurrence matrix Distance :
Min. distance from central pixel (width=2*d+1):
Max. distance from central pixel (height=2*d+1):
Compute Statistical Features?
Horizontal distance from central pixel (width=2*d+1):
Vertical distance from central pixel (height=2*d+1):