
TL;DR
This paper introduces a novel, efficient, and secure distributed system for 3D medical image segmentation, addressing computational time and security concerns while maintaining high accuracy.
Contribution
It proposes a new distributed approach for 3D segmentation that accelerates processing and enhances security, filling a gap in existing research.
Findings
Comparable segmentation accuracy to state-of-the-art methods
Reduced execution time through distributed processing
Enhanced security with multimedia algorithms
Abstract
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…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Advanced Image and Video Retrieval Techniques · AI in cancer detection
