Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images

  • 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
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 quantitativeestimation of fibrosis region.
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. Mexican Journal of Biomedical Engineering, 38(1), 155-165. Retrieved from https://rmib.com.mx/index.php/rmib/article/view/19
Section
Special Issue