Secure 3D medical Imaging
This work addresses the need for efficient and secure 3D image segmentation, which is important for domains like health systems and military applications, but it appears incremental as it builds on existing HMM methods with distributed computing and security enhancements.
The paper tackles the problem of inefficient 3D image segmentation by proposing a distributed system that partitions the process across multiple machines, using concepts from distributed multimedia networks to accelerate training of Hidden Markov Models (HMMs) and incorporating secure transmission with bidirectional multimedia security algorithms. The result is a system that achieves comparable efficiency to state-of-the-art methods in terms of segmentation accuracy, security, and execution time, as demonstrated through extensive experiments.
Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multi-media network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time.