Towards A Better Performance for Medical Image Retrieval Using An Integrated Approach

In this paper, we propose an integrated approach for medical image retrieval. In particular, we present a series of experiments in medical image retrieval task. There are three main goals for our participation of this task. First, we will test traditional well-known weighting models used in text retrieval domain, such as BM25, TFIDF and Language Model (LM), for context-based image retrieval. Second, we will evaluate statistical-based feedback models and ontology-based feedback models. Third, we will investigate how content-based image retrieval can be integrated with these two basic technologies of traditional text retrieval. The experimental results have shown that 1) traditional weighting models can work well in context-based medical image retrieval task especially when the parameters are tuned properly; 2) statistical-based feedback models can improve the retrieval performance when a small number of documents are used; however, the medical image retrieval can not benefit from ontology-based query expansion; 3) the retrieval performance can be slightly boosted by integrating content features.