A model for the relationship between semantic and content based similarity using LIDC
Grace M. Dasovich, Robert Kim, Daniela Stan Raicu, Jacob David Furst · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
There is considerable research in the field of content-based image retrieval (CBIR); however, few of the current systems incorporate radiologists' visual impression of image similarity. Our objective is to bridge the semantic gap between radiologists' ratings and image features. We have been developing a conceptual-based similarity model derived from content-based similarity to improve CBIR. Previous work in our lab reduced the Lung Image Database Consortium (LIDC) data set into a selection of 149 images of unique nodules, each containing nine semantic ratings by four radiologists and 64 computed image features. After evaluating the similarity measures for both content-based and semantic-based features, we selected 116 nodule pairs with a high correlation between both similarities. These pairs were used to generate a linear regression model that predicts semantic similarity with content similarity input with an R2 value of 0.871. The characteristics and features of nodules that were used for the model were also investigated.