Suporte a sistemas de auxílio ao diagnóstico e de recuperação de imagens por conteúdo usando mineração de regras de associação / Supporting Computer-Aided Diagnosis and Content-Based Image Retrieval Systems through Association Rule Mining
AUTOR(ES)
Marcela Xavier Ribeiro
DATA DE PUBLICAÇÃO
2008
RESUMO
In this work we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to diminish the semantic gap that exists between low-level features and its high-level semantical meaning. The StARMiner (Statistical Association Rule Miner) algorithm was developed to associate low-level features with their semantical meaning. StARMiner is also employed to perform feature selection in medical image datasets, improving the precision of CBIR systems. To improve CAD systems, we developed the IDEA (Image Diagnosis Enhancement through Association rules) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can accelerate the process of diagnosing or strengthen a hypothesis, giving to the physician a statistical support to the decision making process. Two new algorithms are developed to support the IDEA method: to pre-process low-level features and to propose a diagnosis based on association rules. We performed several experiments to validate the developed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems
ASSUNTO(S)
association rule mining mineração de imagens image mining mineração de regras de associação auxílio ao diagnóstico médico computer-aided diagnosis
