Elastogram features selection and classification based on mRMR and SVM
Yingtao Zhang · Ha'erbin gongye daxue xuebao · 2012
For evaluating elastogram objectively,image processing and pattern recogniton techniques are proposed.First the real elasticity information encoded in color was extracted by transform the image from RGB color space to HSV space.Then the statistical features and texture features were extracted from region of interest on the elastogram.The important and reliable features were selected by using Minimum-Redundancy-Maximum-Relevance(mRMR) algorithm.Finally the selected features were input to the SVM classifier to classify the thyroid nodules into benign and malignant.The experiment results confirmed the method had higher accuracy(92%).It is helpful to improve the clinical accuracy by using CAD techniques.