Wenjing Zhao* and Feng Shi Pages 128 - 134 ( 7 )
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.
Boundary detection, data scaling, image segmentation, mediastinal lymph node, object detection, random forest.
College of Sciences, Northeastern University, Shenyang, College of Computer Science and Engineering, Northeastern University, Shenyang