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. Graphical Abstract Graphical abstract of the proposed enhancement method.

中文翻译：

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使用模糊伽玛推理模型的低对比度X射线增强。

X射线图像在为医师提供与骨折和疾病相关的令人满意的信息方面起着重要作用；不幸的是，这些图像大多数都具有对比度低和质量差的缺点。因此，图像的增强将增加关于自主诊断系统的病理学的正确信息的准确性。本文提出了一种基于模糊伽马推理模型（FGRM）的基于亮度调节的低对比度X射线图像增强新方法。为此，我们考虑了三个阶段：预处理，自适应伽玛校正（GC）的模糊模型以及基于盲参考的质量评估。通过基于统计量度（BR-SM）和分散位置（BR-DL）描述符的两种不同的盲目参考方法来检验所提出方法的准确性，得到的图像支持。所提出的FGRM方法在三个数据库（子宫颈，腰椎和手部X射线照片）上的实验结果在对比度调整和提供令人满意的高质量图像方面产生了令人满意的结果。图形摘要所建议的增强方法的图形摘要。