@InProceedings{MenezesAraśConc:2021:ApBaIm,
               author = "Menezes, Luiza C. de and Ara{\'u}jo, Augusto R. V. F. de and 
                         Conci, Aura",
          affiliation = "Universidade Federal Fluminense, Brazil  and Universidade Federal 
                         Fluminense, Brazil  and Universidade Federal Fluminense, Brazil",
                title = "An approach based on image processing techniques to segment lung 
                         region in chest X-ray images",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "
                         
                         lung-segmentation,image-processing,mathematical-morphology,x-ray,cxr.",
             abstract = "Chest X-ray (CXR) images help specialists worldwide to diagnose 
                         lung diseases, such as tuberculosis and COVID-19. A primary step 
                         in an image-based diagnostic tool is to segment the region of 
                         interest. That facilitates the disease classification problem by 
                         reducing the amount of information to be processed. However, due 
                         to the noisy nature of CXRs, identifying the lung region can be a 
                         challenging task. This paper addresses the lung segmentation 
                         problem using a less costable computational process based on image 
                         analysis and mathematical morphology techniques. The proposed 
                         method achieved a specificity of 92.92%, a Jaccard index of 
                         77.77%, and a Dice index of 87.37% on average. All images that 
                         comprehend the dataset used and their respective ground truths are 
                         available for download at 
                         https://github.com/mnzluiza/Lung-Segmentation.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00024",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00024",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CDN4S",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CDN4S",
           targetfile = "2021174449.pdf",
        urlaccessdate = "2024, May 07"
}