title: Three-dimensional Content-Based Cardiac Image Retrieval using global and local descriptors.
abstract: The increase in volume of medical images generated and stored has created difficulties in accurate image retrieval. An alternative is to generate three-dimensional (3D) models from such medical images and use them in the search. Some of the main cardiac illnesses, such as Congestive Heart Failure (CHF), have deformation in the heart's shape as one of the main symptoms, which can be identified faster in a 3D object than in slices. This article presents techniques developed to retrieve 3D cardiac models using global and local descriptors within a content-based image retrieval system. These techniques were applied in pre-classified 3D models with and without the CHF disease and they were evaluated by using Precision vs. Recall metric. We observed that local descriptors achieved better results than a global descriptor, reaching 85% of accuracy. The results confirmed the potential of using 3D models retrieval in the medical context to aid in the diagnosis.
authors.affiliation: Laboratory of Computer Applications for HealthCare, Department of Electrical Engineering - Polytechnic School, University of São Paulo (PPGEE-EP-USP), leila.cristina@usp.br.
authors.forename: Leila C C
authors.initials: LC
authors.lastname: Bergamasco
authors.affiliation: Laboratory of Computer Applications for HealthCare, Department of Electrical Engineering - Polytechnic School, University of São Paulo (PPGEE-EP-USP), leila.cristina@usp.br; Graduate Program in Information Systems - School of Arts, Sciences and Humanities University of São Paulo (EACH-USP), fatima.nunes@usp.br.
authors.forename: Fátima L S
authors.initials: FL
authors.lastname: Nunes
mesh_terms: D000465:Algorithms; D003933:Diagnosis; D006801:Humans; D021621:Imaging, Three-Dimensional; D016247:Information Storage and Retrieval