Adjusted Polynomial Features for Analysis of Lung Ct Images

We introduced new polynomial features described as polynomials on the image pixels domain. After imposing natural conditions these features transform into linear combinations of the image autocovariance function readings. We proposed a way to adjust these features using learning sample textural properties. Experiments on a real diagnostic dataset of lung CT images showed a decrease of the error probability in comparison with the previous works.