Fractures are common injuries, some complicated fractures may require a surgical intervention. When such an
operation is planned it can be beneficial to have access to similar past cases including follow ups to compare, which
method might be the most adapted one in a particular situation. At the orthopaedic service of the University
hospitals of Geneva a database of past cases including pre- and post-operative images and case descriptions has
been created over the past years with the goal to support clinical decision making.
Images play an important role in the decision making process and the judgment of a fracture, but visual
image content is currently not directly accessible for search. At the moment, search is mainly via a classification
system of the fractures or in the patient record itself only by patient ID. In this paper we propose a solution
that combines visual information from several images in a case to calculate similarity between cases and allow
thus an access to visually similar cases. Such a system can complement the text- or classification-based search
that has been used so far.
In a preliminary study, we used pixel-grid-based salient-point features to build a first prototype of case-based
visual retrieval of fracture cases. Cases belonging to different fracture classes were beforehand often confused
due to the similar bone structures in the various images. In this article, a multi-scale approach is used in
order to perform similarity measures at both large and small scales. When compared to the first prototype, the
introduction of scale and spatial information allowed improving the performance of the system. Cases containing
similar bone structures but with dissimilar fractures are generally ranked lower whereas more relevant cases are
returned. The system can thus be expected to perform sufficiently well for use in clinical practice and particularly
for teaching.
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