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Chest Radiograph Image Enhancement: A Total Variation Approach
Author(s) -
Matilda Wilson,
Ian A. Yang,
HenriPierre Charles,
A Kara Peter
Publication year - 2017
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2017913466
Subject(s) - computer science , chest radiograph , variation (astronomy) , image (mathematics) , artificial intelligence , computer vision , radiography , radiology , medicine , physics , astrophysics
Wavelet denoising of medical images relies on the technique of thresholding. A disadvantage of this method is that even though it adequately removes noise in an image, it introduces unwanted artifacts into the image near discontinuities due to Gibbs phenomenon. A total variation method for enhancing chest radiographs is implemented. The approach focuses on lung nodules detection using chest radiographs (CRs) and the method achieves high image sensitivity and could reduce the average number of false positives radiologists encounter.

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