Fuzzy Divergence for Lung Radiography Image Enhancement

W. P. Sousa, C. C. P. Cruz, R. S. Lanzillotti


Segmentation is one of the inferential applications for detecting patterns in
digital images, which has been widely used in the health area. Thresholding, a type of segmentation, consists of separating the gray groups of an image, through one or more thresholds applied to the histogram. Thus, we used the gray tone with the lowest Fuzzy Divergence found to apply the enhancement method, through membership values. This paper presents a method to assist physicians in interpreting lung radiography images, especially in the pandemic caused by COVID-19, when enhancing lung images. In addition, we consulted with a group of medical experts who saw an improvement in image quality, providing the perception of detail in the enhanced image compared to the original image.


image enhancement; fuzzy divergence; covid-19

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DOI: https://doi.org/10.5540/tcam.2023.024.04.00699

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