Fuzzy Divergence for Lung Radiography Image Enhancement

Authors

DOI:

https://doi.org/10.5540/tcam.2023.024.04.00699

Keywords:

image enhancement, fuzzy divergence, covid-19

Abstract

Segmentation is one of the inferential applications for detecting patterns in
digital images, which has been widely used in the health area. Thresholding, a type of segmentation, consists of separating the gray groups of an image, through one or more thresholds applied to the histogram. Thus, we used the gray tone with the lowest Fuzzy Divergence found to apply the enhancement method, through membership values. This paper presents a method to assist physicians in interpreting lung radiography images, especially in the pandemic caused by COVID-19, when enhancing lung images. In addition, we consulted with a group of medical experts who saw an improvement in image quality, providing the perception of detail in the enhanced image compared to the original image.

References

N. A. Memon, A. M. Mirza, and S. Gilani, “Segmentation of lungs from ct scan images for early diagnosis of lung cancer,” in Proceedings of world academy of science, engineering and technology, vol. 14, pp. 228–233, 2006.

Z. Yin, R. Bise, M. Chen, and T. Kanade, “Cell segmentation in microscopy

imagery using a bag of local bayesian classifiers,” in 2010 IEEE International

Symposium on Biomedical Imaging: From Nano to Macro, pp. 125–128, IEEE,

M. Prastawa, E. Bullitt, S. Ho, and G. Gerig, “A brain tumor segmentation

framework based on outlier detection,” Medical image analysis, vol. 8, no. 3,

pp. 275–283, 2004.

S. Hu, E. A. Hoffman, and J. M. Reinhardt, “Automatic lung segmentation

for accurate quantitation of volumetric x-ray ct images,” IEEE transactions on

medical imaging, vol. 20, no. 6, pp. 490–498, 2001.

R. Drake, Gray s anatomia para estudantes 2a edição. Elsevier Brasil, 2010.

B. Abraham and M. S. Nair, “Computer-aided detection of covid-19 from x-ray images using multi-cnn and bayesnet classifier,” Biocybernetics and biomedical engineering, vol. 40, no. 4, pp. 1436–1445, 2020.

T. Chaira and A. K. Ray, Fuzzy Image Processing and Applications with MAT-

LAB. Taylor & Francis Group, LLC, 2009.

R. C. Gonzalez and R. E. Woods, Processamento de imagens digitais. Editora Blucher, 2000.

K. S. Augusto, Identificação Automática do Grau de Maturação de Pelotas de Minério de Ferro. PhD thesis, 2012. Dissertação (Mestrado em Engenharia de Materiais e de Processos Químicos e Metalúrgicos).

L. A. Zadeh, “Fuzzy sets,” 1965.

P. Simons, “Łukasiewicz and the several senses of possibility,” European Review, vol. 23, no. 1, pp. 114–124, 2015.

R. MERLI and L. ALMEIDA, “Nem tudo é tão certo como parece ser: a

matemática fuzzy como linguagem,” ENCONTRO PARANAENSE DE EDUCAÇÃO MATEMÁTICA, XI, 2011.

L. C. de Barros and R. C. Bassanezi, Tópicos de lógica fuzzy e biomatemática. Grupo de Biomatemática, Instituto de Matemática, Estatística e Computação, 2010.

H.-J. Zimmermann, Fuzzy set theory—and its applications. Springer Science & Business Media, 2001.

P. A. Morettin and W. d. O. Bussab, Estatística Básica. Saraiva, 6 ed., 2014.

A. Conci, “Aula 2 – importância do histograma em analise de imagens,” 2015.

T. Chaira and A. K. Ray, “Segmentation using fuzzy divergence,” Pattern

Recognition Letters, vol. 24, no. 12, pp. 1837–1844, 2003.

N. R. Pal and S. K. Pal, “Entropy: A new definition and its applications,” IEEE transactions on systems, man, and cybernetics, vol. 21, no. 5, pp. 1260–1270, 1991.

C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J., vol. 27, pp. 379–423, 1948.

J. Fan and W. Xie, “Distance measure and induced fuzzy entropy,” Fuzzy sets and systems, vol. 104, no. 2, pp. 305–314, 1999.

Downloads

Published

2023-11-27

How to Cite

Sousa, W. P., Cruz, C. C. P., & Lanzillotti, R. S. (2023). Fuzzy Divergence for Lung Radiography Image Enhancement. Trends in Computational and Applied Mathematics, 24(4), 699–716. https://doi.org/10.5540/tcam.2023.024.04.00699

Issue

Vol. 24 No. 4 (2023)

Section

Original Article

License

Copyright

Authors of articles published in the journal Trends in Computational and Applied Mathematics retain the copyright of their work. The journal uses Creative Commons Attribution (CC-BY) in published articles. The authors grant the TCAM journal the right to first publish the article.

Intellectual Property and Terms of Use

The content of the articles is the exclusive responsibility of the authors. The journal uses Creative Commons Attribution (CC-BY) in published articles. This license allows published articles to be reused without permission for any purpose as long as the original work is correctly cited.

The journal encourages Authors to self-archive their accepted manuscripts, publishing them on personal blogs, institutional repositories, and social media, as long as the full citation is included in the journal's website version.