Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images

Authors

  • D. E. Rodríguez Obregón Facultad de Ciencias, Universidad Autónoma de San Luis Potosí,
  • A. R. Mejía Rodríguez Facultad de Ciencias, Universidad Autónoma de San Luis Potosí,
  • G. Dorantes Méndez Facultad de Ciencias, Universidad Autónoma de San Luis Potosí,
  • E. R. Arce Santana Facultad de Ciencias, Universidad Autónoma de San Luis Potosí,
  • S. Charleston Villalobos Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana - Iztapalapa
  • M. Mejía Ávila Instituto Nacional de Enfermedades Respiratorias
  • H. Mateos Toledo Instituto Nacional de Enfermedades Respiratorias
  • R. González Camarena Departamento de Ciencias de la Salud, Universidad Autónoma Metropolitana - Iztapalapa
  • A. T. Aljama Corrales Departamento de Ingeniería Eléctrica, Universidad Autónoma Metropolitana - Iztapalapa

DOI:

https://doi.org/10.17488/RMIB.38.1.11

Keywords:

Pulmonar Fibrosis Estimation, Computed Tomography, Chan-Vese, Medical Image Segmentation

Abstract

A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis region with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitative estimation of fibrosis region.

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Published

2017-01-15

How to Cite

Rodríguez Obregón, D. E., Mejía Rodríguez, A. R., Dorantes Méndez, G., Arce Santana, E. R., Charleston Villalobos, S., Mejía Ávila, M., Mateos Toledo, H., González Camarena, R., & Aljama Corrales, A. T. (2017). Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images. Revista Mexicana De Ingenieria Biomedica, 38(1), 155–165. https://doi.org/10.17488/RMIB.38.1.11

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