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

Author(s)

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

• Document type: Journal article
• Source: 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2019-01-01.

Abstract

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)

• FAPESP's process: 18/24414-2 - A framework for integration of feature extraction techniques and complex databases for MIVisBD
• Grantee: Mirela Teixeira Cazzolato
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 16/17078-0 - Mining, indexing and visualizing Big Data in clinical decision support systems (MIVisBD)
• Grantee: Agma Juci Machado Traina
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/06228-7 - Detection of patterns and anomalies in medical data using Mathematical Modeling
• Grantee: Bruno Squizato Faiçal
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 16/17330-1 - Storage and Navigation Operations on Graphs in Relational DBMS
• Grantee: Lucas de Carvalho Scabora
• Support Opportunities: Scholarships in Brazil - Doctorate