Machine Learning on membrane detection problem

After the reasonable classification of the tougher Brodatz dataset, we move on to a bit tougher problem of membrane detection. There is some ground truth markup of the membranes done by Liz. The dataset can be found in the following location
/usr/sci/crcnsdata/CRCNS/Synapses/data/CellMembraneDetection
The example raw image and the ground truth markup image is shown below

Experiment setup: For this experiment there will be no key point generation. We will iterate through all the pixels in the image and learn over them. The attributes used will be the moments and the histogram bin values for different sized regions. We will be learning using the perceptron based linear classifier and the Decision stump models. The output will also be a image similar to the ground truth image. An overlay image like the one shown below(Ground truth markup over the original) will be created with the predicted membranes.

The feature vectors have been created for the image for all the points on the image with disk Sizes of 5, 10, 15, 20, 25. The feature vector has been stored in the following location.
/usr/sci/crcnsdata/CRCNS/Synapses/data/CellMembraneDetection/
Feature vector: stom-003-regionAttributes-diskSizes-5-10-15-20-25.mat
Y vector: stom-003_clahe_diff_thresh_concomp_thin_edit-yn-25.mat

The attributes look almost inseparable one such attribute is shown in the below figure