Content-Based Medical Image Retrieval Using Low-Level Visual Features and Modality Identification Juan C. Caicedo, Fabio A. Gonzalez and Eduardo Romero BioIngenium Research Group National University of Colombia {jccaicedoru,fagonzalezo,edromero}@unal.edu.co http://www.bioingenium.unal.edu.co Abstract. This paper presents the image retrieval results obtained by the BioIngenium Research Group, in the frame of the ImageCLEFmed 2007 edition. The applied approach consists of two main phases: a pre- processing phase, which builds an image category index and a retrieval phase, which ranks similar images. Both phases are based only on visual information. The experiments show a consistent frame with theory in content-based image retrieval: filtering images with a conceptual index outperforms only-ranking-based strategies; combining features is better than using individual features; and low-level features are not enough to model image semantics. 1 Introduction Designing and modeling methods for medical image search is a challenging task. Hospitals and health centers are surrounded by a large number of medical im- ages with different types of contents, which are mainly archived in traditional information systems. In the last decade, content-based image retrieval methods have been widely studied in different application domains [1] and particularly, research in the medical field has taken special interest. The ImageCLEFmed is a retrieval challenge in a collection of medical images [2], which is organized yearly to stimulate the development of new retrieval models for heterogeneous document collections containing medical images as well as text. The BioInge- nium Research Group at the National University of Colombia participated in the retrieval task of the ImageCLEFmed 2007 edition [3], using only visual in- formation. Some important issues for retrieving in heterogeneous image collections are a coherent image modeling and a proper problem understanding. Different modal- ities of medical images (radiography, ultrasound, tomography, etc.) could be discriminated using basic low level characteristics such as particular colors, tex- tures or shapes an they are at the base of most image analysis methods. Tra- ditional approaches are mainly based on low-level features which describe the visual appearance of images, because those descriptors are general enough to