Lossless Compression of Medical Image to Overcome Network Congestion Constraints

In recent years there has been tremendous advancement into wireless communication and networking and medical imaging, which has paved a foundation to conceptualize various real-time remote medical application. The constraints of limited bandwidth and objective of desired quality of visual perceptions were always a trade-off. This paper introduces an image encoding technique for diagnostically important region (DIR) by combining an approach of mathematical transformation for dimensional reduction along with Bit plane-by-Bit plane Shift Method. The performance parameters are evaluated with two conventional approaches of arithmetic and Huffman coding with metrics of Bit per pixel and Signal to Noise ratio.

[1]  Isaac N. Bankman,et al.  Handbook of medical image processing and analysis , 2009 .

[2]  Paul Dan Cristea,et al.  Wavelet image compression - the quadtree coding approach , 1999, IEEE Transactions on Information Technology in Biomedicine.

[3]  William A. Pearlman,et al.  Region-based wavelet coding methods for digital mammography , 2003, IEEE Transactions on Medical Imaging.

[4]  Ukrit M.Ferni,et al.  A Survey on Lossless Compression for Medical Images , 2011 .

[5]  Zhe Chen,et al.  Temporal compression for dynamic positron emission tomography via principal component analysis in the sinogram domain , 2003, 2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515).

[6]  Priyanka Sharma,et al.  Performance Analysis of Region of Interest Based Compression Method for Medical Images , 2014, 2014 Fourth International Conference on Advanced Computing & Communication Technologies.

[7]  D. F. W. Yap,et al.  A GUI system for region-based image compression using Principal Component Analysis , 2014, 2014 International Conference on Computational Science and Technology (ICCST).

[8]  V.R.Vijayakuymar,et al.  A Survey on Various Compression Methods for Medical Images , 2012 .

[9]  Aysegül Çuhadar,et al.  Quadtree-based multiregion multiquality image coding , 2004, IEEE Signal Processing Letters.

[10]  I. Maglogiannis,et al.  Region of Interest Coding Techniques for Medical Image Compression , 2007, IEEE Engineering in Medicine and Biology Magazine.

[11]  Jianhua Chen,et al.  2-D Compression of ECG Signals Using ROI Mask and Conditional Entropy Coding , 2009, IEEE Transactions on Biomedical Engineering.

[12]  Murat Kunt,et al.  Rank-order polynomial subband decomposition for medical image compression , 2000, IEEE Transactions on Medical Imaging.

[13]  Xiaolin Wu,et al.  Wavelet coding of volumetric medical images with high throughput and operability , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.