Eze Peter, Parampalli Udaya, Evans Robin, Liu Dongxi
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:6119-6122. doi: 10.1109/EMBC44109.2020.9176066.
Medical image scans and associated electronic medical records (EMR) could be stored locally or transmitted for use in autodiagnosis and remote healthcare in teleradiology. Hence, they require security against unauthorised access and modification. Among other means of providing this security, information hiding (IH) techniques have gained relevance especially for open networks that are prone to active attacks. However, the evaluation of the suitability of these IH algorithms in terms of preserving medical image diagnostic features is currently limited to signal processing parameters. This paper re-interprets existing evaluation parameters and provides a new framework that allows dynamic selection of medical image IH (watermarking and steganography) security algorithms. Specifically, criteria that capture medical statistics used in the diagnosis and monitoring of patients were incorporated. These criteria and framework were validated on the Pneumonia Chest Xray dataset (used in a Kaggle Competition) using three selected IH algorithms that offer privacy and image tamper detection.
医学图像扫描及相关电子病历（EMR）可以本地存储，也可传输用于远程放射学中的自动诊断和远程医疗保健。因此，它们需要防范未经授权的访问和修改。在提供这种安全性的其他手段中，信息隐藏（IH）技术尤其适用于容易遭受主动攻击的开放网络。然而，目前在保留医学图像诊断特征方面对这些IH算法适用性的评估仅限于信号处理参数。本文重新解释了现有的评估参数，并提供了一个新框架，允许动态选择医学图像IH（水印和隐写术）安全算法。具体而言，纳入了用于患者诊断和监测的医学统计数据标准。这些标准和框架在肺炎胸部X光数据集（用于Kaggle竞赛）上使用三种选定的提供隐私和图像篡改检测功能的IH算法进行了验证。
