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Advisor(s)
Abstract(s)
Variational image-processing models offer high-quality processing capabilities for imaging. They have been widely developed and used in the last two decades, enriching the fields of mathematics as well as information science. Mathematically, several tools are needed: energy optimization, regularization, partial differential equations, level set functions, and numerical algorithms. For this work we consider a second-order variational model for solving medical image problems. The aim is to obtain as far as possible fine features of the initial image and identify medical pathologies. The approach consists of constructing a regularized functional and to locally analyse the obtained solution. Some parameters selection is performed at the discrete level in the framework of the finite element method. We present several numerical simulations to test the efficiency of the proposed approach.
Description
Este trabalho foi financiado pelo Concurso Anual para Projetos de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) 2016 do Instituto Politécnico de Lisboa. Código de referência IPL/2016/V2MIP_ISEL
Keywords
Variational method Finite element method Medical image processing
Citation
ALMEIDA, A.; [et al] – Developments on finite element methods for medical image supported diagnostics. In VI ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing. Porto, Portugal: Springer, Cham, – VipIMAGE 2017. ISSN 978-3-319-68194-8. Vol. 27, pp. 275-285
Publisher
SpringerOpen