  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.
