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Benign or Malignant Classification of Lung Nodules Based on Semantic Attributes
GONG Ping, CHENG Yu-hu, WANG Xue-song
ACTA ELECTRONICA SINICA ›› 2015, Vol. 43 ›› Issue (12) : 2476-2483.
PDF(2384 KB)
PDF(2384 KB)
Benign or Malignant Classification of Lung Nodules Based on Semantic Attributes
The current computer aided diagnosis system classifies benign or malignant lung nodules mainly according to the low-level features of lung CT images.However,clinicians use the high-level semantic features of lung CT images.To overcome the inconsistency between the low-level features and high-level semantic features,a new approach of benign or malignant lung nodules classification based on semantic attributes is proposed.Firstly,lung nodule images are extracted using the threshold probability-map method.Secondly,on the one hand,some features including shape,gray,texture,size and position are extracted from lung nodule images to constitute the low-level feature set;on the other hand,according to the experts' annotation of lung nodules,the attributes are extracted to constitute the high-level attribute set.Thirdly,attribute prediction models are built to map the low-level features to the high-level attributes.Finally,the benign or malignant classification of lung nodules is performed using the predicted attributes.Experimental results on the LIDC dataset show that the proposed classification method possesses high classification accuracy and AUC value.
low-level feature / semantic attribute / attribute prediction model / lung nodule / classification {{custom_keyword}} /
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