A multi-feature image retrieval scheme for pulmonary nodule diagnosis

Deep analysis of radiographic images can quantify the extent of intra-tumoral heterogeneity for personalized medicine. In this paper, we propose a novel content-based multi-feature image retrieval (CBMFIR) scheme to discriminate pulmonary nodules benign or malignant. Two types of features are applied to represent the pulmonary nodules. With each type of features, a single-feature distance metric model is proposed to measure the similarity of pulmonary nodules. And then, multiple single-feature distance metric models learned from different types of features are combined to a multi-feature distance metric model. Finally, the learned multi-feature distance metric is used to construct a content-based image retrieval (CBIR) scheme to assist the doctors in diagnosis of pulmonary nodules. The classification accuracy and retrieval accuracy are used to evaluate the performance of the scheme. The classification accuracy is 0.955 ± 0.010, and the retrieval accuracies outperform the comparison methods. The proposed CBMFIR scheme is effective in diagnosis of pulmonary nodules. Our method can better integrate multiple types of features from pulmonary nodules.