Background: Accurate identification lymph nodes in multi-slice CT images enables
promptly diagnosing and correctly treating of cancers and subsequent measuring the effect of the
treatment. Computer-aided detection (CAD) systems are necessary choice to reduce labor intensity of
radiologists and to do the work with higher accuracy than the artificial recognition. The detection of
lymph node is non-trivial since the lymph nodes vary in shape and there is not significant contrast to
their surrounding regions, which makes the effect of the classifiers based on features of either boundaries
or shapes of the lymph nodes unsatisfactory. Recently, the feature extraction from intra lymph
nodes gets more attention than those from the borders and the shapes.
Method: In the paper, the lymph node was segmented by a Random Forest model. 500 random contextual
features were extracted for each voxel of the lymph node. In order to improve performance, we
proposed the scaling features in a Random Forest classifier without any extra complexity.
Result: We testified our method on 10 mediastinum lymph nodes from TCIA (the Cancer Imaging Archive)
database. We improved the performance of the random forest model by the scaled features. After
we adjusted the model parameters and chose for features with high information gains, our Random
Forest classifier reached better performance.
Conclusion: A simpler, faster and more efficient method is searched for enabling practicable computeraided
diagnosis and computer-aided detection in the field of the lymph node segmentation. Since the scaling
could ensure equal treatment of the features with different absolute value in the classifier, the precision
and the recall of our Random Forest classifier were increased based on the scaled features.