Inter-Cluster Features for Medical Image Classification

Feature encoding plays an important role for medical image classification. Intra-cluster features such as bag of visual words have been widely used for feature encoding, which are based on the statistical information within each clusters of local features and therefore fail to capture the inter-cluster statistics, such as how the visual words co-occur in images. This paper proposes a new method to choose a subset of cluster pairs based on the idea of Latent Semantic Analysis (LSA) and proposes a new inter-cluster statistics which capture richer information than the traditional co-occurrence information. Since the cluster pairs are selected based on image patches rather than the whole images, the final representation also captures the local structures present in images. Experiments on medical datasets show that explicitly encoding inter-cluster statistics in addition to intra-cluster statistics significantly improves the classification performance, and adding the rich inter-cluster statistics performs better than the frequency based inter-cluster statistics.