Mouzai Meriem, Tarabet Chahrazed, Mustapha Aouache
Division Télécom, Centre de Développement des Technologies Avancées (CDTA), P.O. Box 17 Baba-Hassen 16303, Algiers, Algeria.
Med Biol Eng Comput. 2020 Jun;58(6):1177-1197. doi: 10.1007/s11517-020-02122-y. Epub 2020 Mar 20.
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.
X射线图像在为医生提供与骨折和疾病相关的满意信息方面发挥着重要作用；不幸的是，这些图像大多对比度低且质量差。因此，图像增强将提高自主诊断系统中关于病变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）的亮度调整的低对比度X射线图像增强新方法。为此，考虑三个阶段：预处理、自适应伽马校正（GC）的模糊模型和基于盲参考的质量评估。通过基于统计量度（BR-SM）和离散-位置（BR-DL）描述符的两种不同盲参考方法，在所得到图像的支持下，检验了所提方法的准确性。所提FGRM方法在三个数据库（颈椎、腰椎和手部X光片）上的实验结果在对比度调整和提供满意质量图像方面产生了良好结果。图形摘要 所提增强方法的图形摘要。
