Optimal transport applied to eye fundus image registration

• Ano de defesa: 2018
• Autor(a) principal: Motta, Danilo Andrade
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Correspondência de grafos; Graph matching; Image processing; Imagens médicas; Medical images; Optimal transport; Processamento de imagem; Registration; Registro; Transporte ótimo
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16042019-083755/

Optimal transport has emerged as a promising and effective tool for supporting modern image processing, geometric processing, and even machine learning. Indeed, the optimal transport theory enables great flexibility in modeling problems, as different optimization resources can be successfully employed while preserving a context relevant property that can be interpreted as mass. In this research, we introduce a novel automatic technique for eye fundus image registration which is based on optimal transport theory, image processing filters, graph matching, and geometric transformations into a concise and unified framework. Given two ocular fundus images, we construct representative graphs which embed in their structures spatial and topological information from the eyes blood vessels. The graphs produced are then used as input by our optimal transport model in order to establish a correspondence between their sets of nodes. We also proposed a new measure that estimates the register quality and an extension of an outlier removal technique called DeSAC. Finally, the best geometric transformation is performed on the image to properly accomplish the registration task. Our method relies on a solid mathematical foundation, is easy-to-implement and performs well when dealing with outliers created during the matching stage, producing deterministic and accurate solutions. We demonstrate the accuracy and effectiveness of the proposed methodology through a comprehensive set of qualitative and quantitative comparisons against various representative state-of-the-art methods on different fundus image databases.