  Feature extraction is a very crucial task in image and pixel (voxel)
classification and regression in biomedical image modeling. In this work we
present a machine learning based feature extraction scheme based on inception
models for pixel classification tasks. We extract features under multi-scale
and multi-layer schemes through convolutional operators. Layers of Fully
Convolutional Network are later stacked on this feature extraction layers and
trained end-to-end for the purpose of classification. We test our model on the
DRIVE and STARE public data sets for the purpose of segmentation and centerline
detection and it out performs most existing hand crafted or deterministic
feature schemes found in literature. We achieve an average maximum Dice of 0.85
on the DRIVE data set which out performs the scores from the second human
annotator of this data set. We also achieve an average maximum Dice of 0.85 and
kappa of 0.84 on the STARE data set. Though these datasets are mainly 2-D we
also propose ways of extending this feature extraction scheme to handle 3-D
datasets.
