Content-Based Retrieval of Medical Images: from Context to Perception

A challenge in Content-Based retrieval of image exams is to provide a timely answer that complies to the specialist's expectation. In many situations, when a specialist gets a new image to analyze, having information and knowledge from similar cases can be very helpful. However, the semantic gap between low-level linage features and their high level semantics may impair the system acceptability In this paper we propose a new method where we gather from the physicians the visual patterns they use to recognize anomalies in images and apply this knowledge not only in the preprocessing of the images, but also on building feature extractors based on these visual patterns. Moreover our approach generates feature vectors with lower dimensionality diminishing the "dimensionality curse" problem. Experiments using computed tomography lung images show that the proposed method improves the precision of the query results up to 75%, and generates feature vectors up to 94% smaller than traditional feature extraction techniques while keeping the same representative power This work shows that perception-based feature extraction combined with the image context can be successfully employed to perform similarity queries in medical image databases.