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.
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Marques, J., Dam, E. (2011). Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_14
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DOI: https://doi.org/10.1007/978-3-642-24319-6_14
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