Abstract
X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologies for an autonomous diagnosis system. In this paper, a new approach for low-contrast X-ray image enhancement based on brightness adjustment using a fuzzy gamma reasoning model (FGRM) is proposed. To achieve this, three phases are considered: pre-processing, Fuzzy model for adaptive gamma correction (GC), and quality assessment based on blind reference. The proposed approach’s accuracy is examined through two different blind reference approaches based on statistical measures (BR-SM) and dispersion-location (BR-DL) descriptors, supported by resulting images. Experimental results of the proposed FGRM approach on three databases (cervical, lumbar, and hand radiographs) yield favorable results in terms of contrast adjustment and providing satisfactory quality images.
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Funding
This work is financially supported in parts by the DGRSDT (Direction Générale de la Recherche Scientifique et du Développement Technologique) and CDTA (Centre de Développement des Technologies Avancées) Algeria, as part of the CDTA’s triennial research protocol 2019-2021. (Project code: N0 04-5/SIA/DTELECOM/CDTA/PT 19-21).
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All data examined in this study involving human X-ray images were maintained by Lister Hill National Center of Biomedical Communications in the National Library of Medicine (NLM) at the National Institutes of Health (NIH) and the Children Hospital, Los Angeles, in accordance with the ethical standards of both institutions.
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Mouzai, M., Tarabet, C. & Mustapha, A. Low-contrast X-ray enhancement using a fuzzy gamma reasoning model. Med Biol Eng Comput 58, 1177–1197 (2020). https://doi.org/10.1007/s11517-020-02122-y
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DOI: https://doi.org/10.1007/s11517-020-02122-y