Content-based Queries on the Casimage Database with the IRMA framework: A Field Report

Recent research has suggested that there is no general similarity measure which can be applied parameter free on arbitrary databases. In contrast the optimal combination of similarity measures and parameters must be identified for each new image repository. This optimization loop is time consuming and depends on the experience of the designer as well as the knowledge of the medical expert. It would be useful if results that have been obtained for one dataset could be transferred to another image repository without extensive re-design of all relevant components. Transfer of data corpora is vital if image retrieval is integrated into complex environments such as picture archiving and communication systems (PACS). Image retrieval in medical applications (IRMA) is a framework that strictly separates data administration and application logic. This permits an efficient transfer of the data abstraction from one database to another without re-designing the software. It supports the loop of estimating a combination of distance measures, parameter adaption and result visualization, which is characteristic if an image retrieval application is used for varying data corpora. In this work the casimage dataset has been added as a data corpus to the IRMA system. Thereon the query performance has been evaluated without optimization of the currently applied feature combination. It consists of scaled representations of the images, global texture, aspect ratio, and an evaluation of deformation between pixels of different images.