Computational techniques to find and suppress bone from chest radiological images.

Abstract

The proposal of this work is to propose bone suppression techniques in chest images. The most common, but inaccessible, way is through Dual Energy Subtraction (DES). This the technique requires specific hardware to generate and receive di erent energy levels capable of di erentiating materials by atomic number. This work uses GAN to perform bone suppression on X-ray images and aimed to evaluate the performance of the cGAN, train a model to locate the thoracic box, and assess two di erent training techniques for boneless image translation. Based on deep learning the main contribution of this work is to improve the bone shadow elimination delimiting the learning region of the Deep Learning (DL) model. By the contextualization of the bones region, was possible present a metric that measures the model accuracy in an interested region. With this study was possible a more precise metric to evaluate the bone suppression quality. Using the Japanese Society of Radiological Technology (JSRT) this study achieved a PSNR index of 31.604, and a similarity coe cient, known as SSIM of 0.9402. When delimiting the learning region, the results were: 31.9136 for PSNR and 0.9633 for SSIM.