Automatic segmentation of lung cancer based on texture features of PET/CT images

TL;DRAbstract

1794 Objectives It is difficult to accurately define the radiation target for radiation therapy of lung cancer. FDG-PET has been shown to be useful in helping to define such targets, but inter-observer variability in target definition remains. This study investigated whether the incorporation of texture features derived from PET/CT images could be used to automatically segment such targets. Methods PET/CT image features derived from spatial grey-level dependence matrices, neighborhood grey tone difference matrices, Tamura9s textural features, first order statistics and structural methods were investigated. A training set of images of 20 patients was used to determine the best features for discriminating between tumor and normal tissues. Using a K-nearest neighbors (KNN) classifier, the area under the receiver operating curve was calculated (AUC) and used to determine the ability of each feature to distinguish tumor from normal tissue. A decision tree was subsequently trained using KNN

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