Code:

All pieces of code have been thought to perform serial processing 
over a choosen database. For example, in the case of DRIVE database,
over 40 different images

cut
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general tool for cutting iteratively over all set of images
in a database (for example Drive). One may use 'all' of each 
eye-fundus image including black margins and replacing at
out of fov pixel in a neighbourhood the avg value of the other 
inside it and inside the fov or simply cut the original images
to a for example 370x370 (DRIVE database) inside the eye-fundus
image. Whether the first or the second choice is the best has 
not been analyzed


pre_processing
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inputs: images of the database to be processed in order to
obtain H and VE images to the following features extraction

output: two images (H, VE indeed) for each input image


features
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input: a pair of H-VE images for each eye-fundus original image

output: feature vectors stored in a .yml archive to future loading
in learning the neural network (and maybe making easier to export data)


neural_network
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input: a set of .yml file containing feature vectors, one per pixel
of each images used for learning, another set of .yml to be loaded
and then generalized by the neural network, and a set of gold (manually
segmented) eye-fundus images (taking values in [0,255], 1 channel) for
learning reference.
The network used is the standard one implemented in openCV.

output:a predicted and automatically generated eye-fundus segmented 
image (b/w, 1 channel) for each .yml archive given to the neural network
standing for an 'unseen' retinal image, to be predicted


postprocessing
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input: the set of automatically-generated predicted image

output: a post processed image for each input image provided.
Colours standing for:
--black: true negative
--white: true positive
--yellow: false negative
--red: false positive

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RESULTS: this folder show the images obtained at the and of the whole processing.
For performance results check the presentation in the shareproject website. Enjoy


