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A Cloud-based Framework to Secure Medical Image Processing

机译：基于云的框架来保护医学图像处理

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In the last few years, advanced software for processing medical images has gained a great interest in modern medicine. In fact, it provides valuable clinical information, and hence, can significantly improve diagnosis and treatment. Nevertheless, implementing these imaging tools often requires an important capital budget in both IT applications and hardware. This solution can unfortunately cause a dramatic increase in operational expenses and medical costs. To mitigate this problem, medical providers are shifting their interest onto using cloud computing, particularly the Software-as-a-Service (SaaS) model, instead of in-house data centres. In this case, healthcare professionals rely on remote applications delivered by an external provider to process patients' digital records. Interestingly, in this paradigm, consumers are billed based on software utilization. Besides, cloud computing promises to offer a better Quality of Service (QoS), including availability, elasticity, trust, response time, security assurance, etc. Regardless of its significant financial benefits, the transition to the cloud environment gives rise to security and privacy problems, especially in the healthcare domain. Recently, various security measures and mechanisms have been suggested to overcome these challenges and accelerate the adoption of cloud computing services. In this regard, numerous cryptographic techniques are used to safely process digital medical images, techniques that make use of homomorphic cryptosystems, Secret Sharing Schemes (SSS), Service-Oriented Architecture (SOA) and Secure Multi-party Computation (SMC). Although these methods are deemed very promising, they can negatively impact the performance of cloud services. Most precisely, they are not yet mature enough to satisfy Service Level Agreement (SLA) constraints. The main contribution of this study consists of presenting a novel approach to secure cloud-based medical image processing. This proposed solution combines segmentation techniques and genetic algorithms together in one model. Based on this method, we rely on pixel intensity and entropy measurements to split an image into a number of regions to maintain data privacy. The principal reason for using genetic algorithms is to optimize the number of generated regions. Furthermore, we opt for an architecture based on multi-cloud systems and CloudSec module to enable distributed data processing and prevent accidental disclosure of medical information. As shown in the simulation results, the proposal is an appropriate framework for fuelling the integration of cloud applications in the healthcare sector. In particular, it enables clients to securely use remote image processing tools.
机译：在过去的几年中，用于处理医学图像的先进软件对现代医学引起了极大的兴趣。实际上，它提供了有价值的临床信息，因此可以显着改善诊断和治疗。但是，实施这些映像工具通常需要在IT应用程序和硬件上投入大量的资本预算。不幸的是，该解决方案会导致运营费用和医疗费用的急剧增加。为了缓解此问题，医疗提供商将他们的兴趣转移到使用云计算，特别是软件即服务（SaaS）模型，而不是内部数据中心。在这种情况下，医疗保健专业人员依靠外部提供商提供的远程应用程序来处理患者的数字记录。有趣的是，在这种范例中，根据软件使用情况向消费者收费。此外，云计算有望提供更好的服务质量（QoS），包括可用性，弹性，信任度，响应时间，安全保证等。尽管具有显着的财务收益，但向云环境的过渡会带来安全性和隐私性。问题，尤其是在医疗领域。最近，已经提出了各种安全措施和机制来克服这些挑战并加速云计算服务的采用。在这方面，许多加密技术用于安全处理数字医学图像，利用同态加密系统，秘密共享方案（SSS），面向服务的体系结构（SOA）和安全多方计算（SMC）的技术。尽管这些方法被认为非常有前途，但它们可能会对云服务的性能产生负面影响。最准确地说，它们还不够成熟，无法满足服务水平协议（SLA）约束。这项研究的主要贡献在于提出一种新颖的方法来保护基于云的医学图像处理。该提出的解决方案将分割技术和遗传算法结合在一个模型中。基于此方法，我们依靠像素强度和熵测量将图像划分为多个区域以维护数据隐私。使用遗传算法的主要原因是为了优化生成区域的数量。此外，我们选择基于多云系统和CloudSec模块的架构，以实现分布式数据处理并防止意外泄露医疗信息。如仿真结果所示，该建议是促进医疗保健领域云应用程序集成的适当框架。特别是，它使客户端能够安全地使用远程图像处理工具。

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