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Soft Computing

, Volume 23, Issue 19, pp 9265–9286 | Cite as

New level set approach based on Parzen estimation for stroke segmentation in skull CT images

  • Elizângela de S. Rebouças
  • Regis C. P. Marques
  • Alan M. Braga
  • Saulo A. F. Oliveira
  • Victor Hugo C. de AlbuquerqueEmail author
  • Pedro P. Rebouças Filho
Focus

Abstract

Stroke is the second most common cause of death and one of the leading causes of disability in industrialized countries. Among the 17.5 million deaths caused by cardiovascular disease in 2012, approximately 6.7 million were caused by stroke. This study is focused on the hemorrhagic type of stroke, which accounts for 40% of all stroke deaths. This work proposes a new approach to segment the stroke from cranial CT images, in order to aid medical diagnosis. This approach proposes to automatically start the level set method within the stroke region and to use a nonparametric estimation approach based on the Parzen window to segment the stroke. The results obtained by the proposed approach are compared with the results of the level set algorithms using fuzzy C-means, and the level set based on the method of coherent propagation, fuzzy C-means, Watershed and Region Growth, which are commonly used in this field. The assessment is based on the validation of the segmentation from a radiologist. The experimental results showed that the proposed method presented a superior performance compared to the other commonly used methods, thus indicating that it is a promising tool for medical diagnosis. The results show that the proposed method has the highest mean of accuracy with 99.84% and lowest standard deviation of 0.08%, demonstrating that the proposed method is superior to the others in the literature. These results are confirmed by the high indexes of accuracy, sensitivity and specificity.

Keywords

Stroke region segmentation Parzen window Level set Aid to medical diagnosis 

Notes

Acknowledgements

The authors acknowledge the financial support and encouragement from the Brazilian National Council for Research and Development (CNPq). The authors thank the Graduate Program in Computer Science from the Instituto Federal do Ceará and the Department of Computer Engineering and Walter Cantídio University Hospital of Universidade Federal do Ceara for technical support in Pulmonology and images. The last author acknowledges the sponsorship from the Federal Institute of Education, Science and Technology of Ceara via Grants PROINFRA/2017 and PROINFRA PPG/2017.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto Federal de Federal de Educação, Ciência e Tecnologia do Ceará (IFCE)CearáBrazil
  2. 2.Programa de Pós-Graduação em Informática Aplicada, Laboratório de BioinformáticaUniversidade de FortalezaFortalezaBrazil

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