UCORM: Indexing Uncorrelated Metric Spaces for Concise Content-Based Retrieval of Medical Images

Autor(es)

Zabot, Guilherme F. ; Cazzolato, Mirela T. ; Scabora, Lucas C. ; Faical, Bruno S. ; Traina, Agma J. M. ; Traina, Caetano, Jr. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2019-01-01.

Resumo

The large amount of medical exams generated by hospitals has a great potential to boost the support for physicians on decision making tasks. This requires efficient and reliable computational systems to retrieve relevant information in real-time. Existing Content-Based Image Retrieval (CBIR) systems rely on Metric Access Methods (MAMs) to speed-up the retrieval task. In this context, images are represented by Feature Extraction Methods (FEMs), according to information such as color or texture. However, MAMs usually index images based on a single FEM. Whenever physicians want to search for similar images using multiple FEMs simultaneously, they need to perform separated queries. In this work, we propose UCORM, an access method capable of indexing images using multiple FEMs by overlapping different metric spaces. UCORM selects the best FEMs to generate a concise yet accurate indexing space. It relies on an interesting use of Pearson correlation, that we named PCMS, to compute the correlation between different FEMs. PCMS allows UCORM to improve the retrieval task by minimizing the overlapping between metric spaces, resulting on fewer intermediary images when performing a query. Experimental analysis shows that UCORM prunes well the data distribution regions with low correlation between FEMs. Also, two medical application scenarios support our claim that UCORM is well-fitted for clinical environments. (AU)

• Processo FAPESP: 18/24414-2 - Ambiente para integração de técnicas para a extração de características e bases de dados complexos para o projeto MIVisBD
• Beneficiário: Mirela Teixeira Cazzolato
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 18/06228-7 - Detecção de padrões e anomalias em dados médicos usando Modelagem Matemática
• Beneficiário: Bruno Squizato Faiçal
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 16/17330-1 - Armazenamento e Operações de Navegação em Grafos em SGBDs Relacionais
• Beneficiário: Lucas de Carvalho Scabora
• Modalidade de apoio: Bolsas no Brasil - Doutorado