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Abstract
Obstructive Sleep Apnea(OSA) is breathing disorder syndrome in which the airway tract pauses during sleep due to collapse of pharyngeal airway. It is occurred at the sleep time, with fourth dimensional high resolution in airway tract Obstruction in children and adults with OSA. Here, we the operator places the seeds that includes the Oesopharyngeal air tract and found out a threshold for the first frame in order to determine the affected tissues which blocks the patients pharyngeal tract. In this automated segmentation method it shows the process of MRI studies of the pharyngeal air pathway and enable diagnose of obstructive tissues with the collapse tissues. Region growing method results well in Dice Coefficients compared with manual segmentation. It automatically detects 90% of collapse tissues. This approach leads to segment the pharyngeal pathway correctly. It uses long MRI scans in order to diagnosis the collapsed tissues with graph, accurate details and coefficients in a short span of duration.
References
Muthukumaran, N., & Ravi, R. (2015). The Performance Analysis of Fast Efficient Lossless Satellite Image Compression and Decompression for Wavelet Based Algorithm. Wireless Personal Communications, vol. 81, no. 2, pp. 839-859, SPRINGER.
Muthukumaran, N., & Ravi, R. (2015). VLSI Based Image Acquisition using Block Based Fast Efficient Compression Algorithm. International Arab Journal of Information Technology, vol. 12, no. 4, pp. 333-339.
Muthukumaran, N., & Ravi, R. (2014). Simulation based VLSI implementation of fast efficient lossless image compression system using Adjusted Binary Code & Golumb Rice Code. World Academy of Science, Engineering and Technology, vol. 8, no.9, pp. 1603- 1606.
Akter, M., Reaz, M. B. I., Mohd Yasin. F., & Choong. F. (2008). A modified set partitioning in hierarchical trees algorithm for real compression. Journal of Communication Technology Electronics, vol. 53, no. 6, pp. 642-650.
Ansari, M. A., & Ananda, R. S. (2009). Context based
medical image compression for ultrasound
images with contextual set partitioning in hierarchical trees algorithm. Advance Engineering Software, vol. 40, no. 7, pp. 487-496.
Andra, K., Acharya.T., & Chakrabarti.C. (2000). A multi-bit binary arithmetic coding technique. In Proc.
Int. Conf. Image Process., Vancouver, BC, Canada,
vol. 1, pp. 928-931.
Cao, B., Li, Y.S., & Liu, K. (2004). VLSI architecture of MQ encoder in JPEG 2000. Journal of Xidian Xuebao, vol. 31, no. 5, pp. 714-718.
Corsonello, P., Perri, S., Zicari, P., & Cocorullob, G. (2005). Microprocessor based FPGA implementation of SPIHT image compression subsystems. Microprocessor and Microsystems. vol. 29, no. 6, pp. 299-305.
Devangkumar Shah, & Chandresh Vithlani. (2014). VLSI Oriented Lossy Image Compression approach using DA-Based 2D-Discrete Wavelet”, in International Arab Journal of Information Technology, vol. 11, no. 1, pp. 59-68.
Fry, T. W., & Hauck. S. A. (2005). SPIHT image compression on FPGAs,” IEEE Trans. Circuits System
for Video Technol., vol. 15, no. 9, pp.1138- 1147.
Muthukumaran. N and Ravi. R, 'Hardware
Implementation of Architecture Techniques for Fast Efficient loss less Image Compression System', Wireless Personal Communications, Volume. 90, No. 3, pp. 1291- 1315, October 2016, SPRINGER.
Muthukumaran. N and Ravi. R, 'The Performance Analysis of Fast Efficient Lossless Satellite Imag