%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2021/09.03.11.28
%2 sid.inpe.br/sibgrapi/2021/09.03.11.28.46
%@doi 10.1109/SIBGRAPI54419.2021.00024
%T An approach based on image processing techniques to segment lung region in chest X-ray images
%D 2021
%A Menezes, Luiza C. de,
%A Araújo, Augusto R. V. F. de,
%A Conci, Aura,
%@affiliation Universidade Federal Fluminense, Brazil
%@affiliation Universidade Federal Fluminense, Brazil
%@affiliation Universidade Federal Fluminense, Brazil
%E Paiva, Afonso ,
%E Menotti, David ,
%E Baranoski, Gladimir V. G. ,
%E Proença, Hugo Pedro ,
%E Junior, Antonio Lopes Apolinario ,
%E Papa, João Paulo ,
%E Pagliosa, Paulo ,
%E dos Santos, Thiago Oliveira ,
%E e Sá, Asla Medeiros ,
%E da Silveira, Thiago Lopes Trugillo ,
%E Brazil, Emilio Vital ,
%E Ponti, Moacir A. ,
%E Fernandes, Leandro A. F. ,
%E Avila, Sandra,
%B Conference on Graphics, Patterns and Images, 34 (SIBGRAPI)
%C Gramado, RS, Brazil (virtual)
%8 18-22 Oct. 2021
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K lung-segmentation,image-processing,mathematical-morphology,x-ray,cxr.
%X 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.
%@language en
%3 2021174449.pdf