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Classification of calcified regions in atherosclerotic lesions of the carotid artery in computed tomography angiography images

  • Danilo Samuel Jodas
  • Aledir Silveira Pereira
  • João Manuel R. S. TavaresEmail author
Original Article

Abstract

The identification of atherosclerotic plaque components, extraction and analysis of their morphology represent an important role towards the prediction of cardiovascular events. In this article, the classification of regions representing calcified components in computed tomography angiography (CTA) images of the carotid artery is tackled. The proposed classification model has two main steps: the classification per pixel and the classification per region. Features extracted from each pixel inside the carotid artery are submitted to four classifiers in order to determine the correct class, i.e. calcification or non-calcification. Then, geometrical and intensity features extracted from each candidate region resulting from the pixel classification step are submitted to the classification per region in order to determine the correct regions of calcified components. In order to evaluate the classification accuracy, the results of the proposed classification model were compared against ground truths of calcifications obtained from micro-computed tomography images of excised atherosclerotic plaques that were registered with in vivo CTA images. The average values of the Spearman correlation coefficient obtained by the linear discriminant classifier were higher than 0.80 for the relative volume of the calcified components. Moreover, the average values of the absolute error between the relative volumes of the classified calcium regions and the ones calculated from the corresponding ground truths were lower than 3%. The new classification model seems to be adequate as an auxiliary diagnostic tool for identifying calcifications and allowing their morphology assessment.

Keywords

Medical imaging Pattern recognition Classification Atherosclerosis 

Notes

Acknowledgements

This work was partially funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), a funding agency in Brazil, under the PhD Grant with reference number 0543/13-6. The authors thank the funding of Project NORTE-01-0145-FEDER-000022—SciTech—Science and Technology for Competitive and Sustainable Industries, co-financed by “Programa Operacional Regional do Norte” (NORTE2020), through “Fundo Europeu de Desenvolvimento Regional” (FEDER).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.CAPES Foundation - Ministry of Education of BrazilBrasíliaBrazil
  2. 2.Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de EngenhariaUniversidade do PortoPortoPortugal
  3. 3.Universidade Estadual Paulista “Júlio de Mesquita Filho”São José do Rio PretoBrazil

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