Multimodal medical image retrieval: OHSU at ImageCLEF 2008

We present results from Oregon Health & Science University's participation in the medical image retrieval task of ImageCLEF 2008. We created a web-based retrieval system built on a full-text index of the annotations using a Ruby on Rails framework. The text-based search engine was implemented in Ruby using Ferret, a port of Lucene. In addition to this textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. All images were annotated with the purported modality. Our system provides the user with a number of search options including those for limiting the search to the desired modality, UMLS-based term expansion and Natural Language Processing based techniques. Purely textual runs as well as mixed runs using the purported modality were submitted. We also submitted interactive runs using a number of user specified search options. Latent semantic analysis of the visual features was used to reorder results. The use of the UMLS Metathesaurus increased our recall. However, our system is primarily geared towards precision. Consequently, many of our multimodal automatic runs using the custom parser as well as interactive runs had high early precision. Our runs also performed well using the bpref metric, a measure that is more robust in the case of incomplete judgments.