Advancements in networking technologies like cloud computing have popularized applications like, Video-On Demand (VOD), video conferencing, pay-per-view, video broadcast, etc, in all such applications, confidentiality of the video data during transmission in network and during storage is extremely important. This necessitates secure video encryption algorithms suitable for multimedia application because of the large data size and real time constraint. Conventional encryption algorithms are designed for generic data, and as such, it does not support many specific video application requirements. In the paper we propose a computationally efficient and secure video encryption algorithm that makes encryption feasible for real-time applications without heavy computational overhead and reduces key management by utilizing block shuffling technique. Block Shuffling based video encryption with Faro IN OUT Shuffle and rotation, i.e., first image is rotated by an angle then key is generated based Block size using Faro IN OUT perfect shuffle which is a perfect shuffling algorithm which is isomorphic to random permutation. we will show that our proposed method provide more scrambling of image then random permutation and no need to maintain long key file.
One of the primary diagnostic and treatment evaluation tools for brain tumors has been magnetic resonance (MR) imaging. MR imaging has become a widely-used method of high quality medical imaging, especially in brain imaging where MR's soft tissue contrast and non-invasiveness are clear advantages. MR images can also be used to track the size of a brain tumor as it responds (or doesn't) to treatment. A reliable method for segmenting tumor would clearly be a useful tool. Currently, however, there is no method widely accepted in clinical practice for quantitating tumor volumes from MR images . Tese manual measurements, have shown poor reproducibility and tumor response criteria based on these manual estimations have shown poor correlation with quantitative 2D and 3D metrics . Computer-based brain tumor segmentation has remained largely experimental work, with approaches including multi-spectral analysis, edge detection, neural networks, and knowledge-based techniques. We propose a real time design for brain tumor detection that a combination of knowledge-based techniques and multi-spectral analysis based on Support Vector Machines. The results shows that the system could effectively detect pathology and label normal transaxial slices.
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