Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection
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.
Keywordsmachine learning PLS classification feature extraction feature selection texture analysis OA bone structure
Unable to display preview. Download preview PDF.
- 12.Bryan, K., Brennan, L., Cunningham, P.: Metafind: A feature analysis tool for metabolomics data. BMC Bioinformatics 9 (2008)Google Scholar
- 17.Weickert, J.: Anisotropic Diffusion in Image Processing. B.G.Teubner Stuttgart (1998)Google Scholar
- 18.Dam, E.B., Loog, M., Christiansen, C., Byrjalsen, I., Folkesson, J., Nielsen, M., Qazi, A., Pettersen, P.C., Garnero, P., Karsdal, M.A.: Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers. Arthritis Research & Therapy 11(4), R115 (2009)CrossRefGoogle Scholar