Objective: To develop a new content-based image retrieval (CBIR) based computer-aided
diagnosis (CAD) scheme to discriminate the lung nodules benign or malignant and to perform a
preliminary evaluation of this CAD scheme and its robustness.
Methods: Two lung nodule datasets from LIDC-IDRI lung CT database were assembled. Two nodule
density related features were computed to represent each nodule. For each queried nodule, a twostep
CBIR scheme was applied to retrieve the top ten most similar reference nodules. A classification
likelihood value was calculated to predict the malignancy of the lung nodule. To assess the robustness
of the CBIR scheme, we first tested this CAD scheme on the second dataset, and then used
the second dataset to retrieve the first dataset. To verify the feasibility of the CBIR scheme, classification
performance of our scheme was comparied with that of classical classifiers.
Results: Through applying a leave-one-out validation method on the first dataset, an area under the
ROC curve (AUC) of 0.915 was obtained, and the total classification accuracy was 83.0%. For robustness
on the second dataset, the AUC was 0.727, and the total classification accuracy was 66.1%.
When we used the second dataset to retrieve the first dataset, the AUC value and the total classification
accuracy were 0.751 and 71.3%, respectively. The classification performance of the proposed
scheme outperforms that of the classical classifiers.
Conclusion: This study demonstrated that (1) a simple and efficient CBIR based CAD scheme applying
two nodule density related features achieved high performance for classification of lung nodules
and (2) this CAD scheme using CBIR approach also had high robustness performance in the
future clinical application.