Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection

Joselene Marques, Erik Dam

Abstract: We present a methodology that applies machine-learning techniques to guide partial least square regression (PLS) for feature extraction combined with feature selection. The developed methodology was evaluated in a framework that supports the diagnosis of knee osteoarthritis (OA). Initially, a set of texture features are extracted from the MRI scans. These features are used for segmenting the region-ofinterest and as input to the PLS regression. Our method uses PLS output to rank the features and implements a learning step that iteratively selects the most important features and applies PLS to transform the new feature space. The selected bone texture features are used as input to a linear classifier trained to separate the subjects in healthy or OA. The developed algorithm selected 18% of the initial feature set and reached a generalization area-under-the-ROC of 0.93, which is higher than established markers known to relate to OA diagnosis.