Another kNN setup

In this the image is divided into 4 quadrants and SIFT key points in each quadrant is used as test points and the SIFT key points from the other quadrants are used to train the classifier.

The statistics about points in each fold is
Fold 1
Total points = 1797, Positive points = 848
Fold 2
Total points = 1831, Positive points = 877
Fold 3
Total points = 1499, Positive points = 486
Fold 4
Total points = 1732, Positive points = 756

The definition of FP_rate has been changed, other definitions remain the same
#TP = number of ground truth positives (synapses marked by the Mark lab) with at least one marking done by the classifier within some radius (10 pixels).
#GTP = number of ground truth points (synapses marked by the Mark lab)
=> TP_rate = #TP / #GTP
#FP = number of positions marked by the classifier - #TP.
=> FP_rate = #FP / (#nSIFTpoints - #GTP}