Department of Medicine, University of Ottawa, Ottawa, Canada.
J Xray Sci Technol. 2009;17(4):335-46. doi: 10.3233/XST-2009-0228.
Today's imaging diagnosis needs to adapt modern techniques of quality engineering to maintain and improve its accuracy and reliability in health care system. One of the main factors that influences diagnostic accuracy of plain film X-ray on detecting pathology is the level of film exposure. If the level of film exposure is not adequate, a normal body structure may be interpretated as pathology and vice versa. This not only influences the patient management but also has an impact on health care cost and patient's quality of life. Therefore, providing an accurate and high quality image is the first step toward an excellent patient management in any health care system. In this paper, we study these techniques and also present a fuzzy intelligent quality monitoring model, which can be used to keep variables from degrading the image quality. The variables derived from chemical activity, cleaning procedures, maintenance, and monitoring may not be sensed, measured, or calculated precisely due to uncertain situations. Therefore, the gamma-level fuzzy Bayesian model for quality monitoring of an image processing is proposed. In order to apply the Bayesian concept, the fuzzy quality characteristics are assumed as fuzzy random variables. Using the fuzzy quality characteristics, the newly developed model calculates the degradation risk for image processing. A numerical example is also presented to demonstrate the application of the model.
今天的影像学诊断需要适应现代质量工程技术，以维持和提高其在医疗保健系统中的准确性和可靠性。影响普通 X 射线摄影检测病理学诊断准确性的主要因素之一是胶片曝光水平。如果胶片曝光水平不足，正常的身体结构可能被解释为病理学，反之亦然。这不仅影响患者的管理，而且对医疗保健成本和患者的生活质量也有影响。因此，提供准确和高质量的图像是任何医疗保健系统中实现卓越患者管理的第一步。在本文中，我们研究了这些技术，并提出了一种模糊智能质量监测模型，可用于防止变量降低图像质量。由于不确定的情况，源于化学活性、清洁程序、维护和监测的变量可能无法被感知、测量或精确计算。因此，提出了用于图像处理质量监测的伽马级模糊贝叶斯模型。为了应用贝叶斯概念，将模糊质量特性假设为模糊随机变量。使用模糊质量特性，新开发的模型计算图像处理的降级风险。还提出了一个数值实例来说明模型的应用。
