Latent Semantic Association for Medical Image Retrieval

In this work, we propose a Latent Semantic Association Retrieval(LSAR) method to break the bottleneck of the low-level feature based medical image retrieval. The method constructs the high-level semantic correlations among patients based on the low-level feature set extracted from the images. Specifically, a Pair-LDA model is firstly designed to refine the topic generation process of traditional Latent Dirichlet Allocation (LDA), by generating the topics in a pair-wise context. Then, the latent association, called CCA-Correlation, is extracted to capture the correlations among the images in the Pair-LDA topic space based on Canonical Correlation Analysis (CCA). Finally, we calculate the similarity between images using the derived CCA-Correlation model and apply it to medical image retrieval. To evaluate the effectiveness of our method, we conduct the retrieval experiments on the Alzheimer´s Disease Neuroimaging Initiative (ADNI) baseline cohort with 331 subjects, and our method achieves good improvement compared to the state-of-the-art medical image retrieval methods. LSAR is independent on problem domain, thus can be generally applicable to other medical or general image analysis.