title: Endowing a Content-Based Medical Image Retrieval System with Perceptual Similarity Using Ensemble Strategy.
abstract: Content-based medical image retrieval (CBMIR) is a powerful resource to improve differential computer-aided diagnosis. The major problem with CBMIR applications is the semantic gap, a situation in which the system does not follow the users' sense of similarity. This gap can be bridged by the adequate modeling of similarity queries, which ultimately depends on the combination of feature extractor methods and distance functions. In this study, such combinations are referred to as perceptual parameters, as they impact on how images are compared. In a CBMIR, the perceptual parameters must be manually set by the users, which imposes a heavy burden on the specialists; otherwise, the system will follow a predefined sense of similarity. This paper presents a novel approach to endow a CBMIR with a proper sense of similarity, in which the system defines the perceptual parameter depending on the query element. The method employs ensemble strategy, where an extreme learning machine acts as a meta-learner and identifies the most suitable perceptual parameter according to a given query image. This parameter defines the search space for the similarity query that retrieves the most similar images. An instance-based learning classifier labels the query image following the query result set. As the concept implementation, we integrated the approach into a mammogram CBMIR. For each query image, the resulting tool provided a complete second opinion, including lesion class, system certainty degree, and set of most similar images. Extensive experiments on a large mammogram dataset showed that our proposal achieved a hit ratio up to 10% higher than the traditional CBMIR approach without requiring external parameters from the users. Our database-driven solution was also up to 25% faster than content retrieval traditional approaches.
authors.affiliation: Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil. bedo@icmc.usp.br.
authors.forename: Marcos Vinicius Naves
authors.initials: MV
authors.lastname: Bedo
authors.affiliation: Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil.
authors.forename: Davi
authors.initials: D
authors.lastname: Pereira Dos Santos
authors.affiliation: Institute of Education, Science and Technology of the Triângulo Mineiro, Uberaba, Brazil.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Ponciano-Silva
authors.affiliation: Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil.
authors.forename: Paulo Mazzoncini
authors.initials: PM
authors.lastname: de Azevedo-Marques
authors.affiliation: Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil.
authors.forename: André Ponce de León
authors.initials: AP
authors.lastname: Ferreira de Carvalho
authors.affiliation: Institute of Mathematics and Computer Science, University of São Paulo, São Paulo, Brazil.
authors.forename: Caetano
authors.initials: C
authors.lastname: Traina
mesh_terms: D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods* / Q000706:statistics & numerical data*; D000069550:Machine Learning; D008327:Mammography / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000706:statistics & numerical data
keywords: Computer-aided diagnosis; Content-based medical image retrieval; Similarity queries