Applying a Conditional GAN for Bone Suppression in Chest Radiography Images

  • Hugo Eduardo Ziviani UFOP
  • Guillermo Cámara Chávez UFOP
  • Mateus Coelho Silva UFOP


Bone suppression in radiography is a suitable technique to evaluate the health of soft tissues in exams. For instance, these techniques are essential in evaluating chest radiography images during the COVID-19 outbreak. The purpose of this work is to propose an alternative to solve the bone suppression task in chest radiography images using Generative Adversarial Networks (GANs). Specifically, we used a conditional GAN type (cGAN) to provide a bone-suppressed version of the initial image. To quantify the results, it was necessary to review the main metrics and some state-of-the-art papers related to ours. We compared our result to works from the literature that used the same dataset as the proposal or related techniques. The most used dataset was the Japanese Society of Radiological Technology (JSRT) in these works. With this set of images, we reached a PSNR index of 34.96, which was better than that reviewed in the literature, and a similarity coefficient, known as SSIM, of 0.94. As for the loss calculated by MS-SSIM, we obtained the lowest compared to the reviewed works.

Palavras-chave: Bone Suppression, Generative Adversarial Networks, cGAN's


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ZIVIANI, Hugo Eduardo; CHÁVEZ, Guillermo Cámara; SILVA, Mateus Coelho. Applying a Conditional GAN for Bone Suppression in Chest Radiography Images. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 49. , 2022, Niterói. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 25-36. ISSN 2595-6205. DOI: