Word Indexing Versus Conceptual Indexing in Medical Image Retrieval

This paper presents our participation in medical image retrieval task of ImageCLEF 2012. Our aim is to study the effectiveness of using conceptual indexing comparing to word indexing in medical image retrieval. For this aim, we have used in the one hand the Terrier tool for textual indexing and for textual retrieval, and on another hand, the MetaMap tool for conceptual indexing and Vector model for conceptual retrieval. More precisely, the run of the BM25 model is considered as a baseline. For textual indexing, we tried to compare different weighting formulas. However, for conceptual indexing, we Used BM25 model results to extract concepts and rerank results using vector model. Results show that the use of the textual indexing is more useful than the conceptual indexing. However, the conceptual indexing improves the result of some queries, which encourages us to continue the study of conceptual indexing and retrieval.