Gray Scale Medical Image Compression using SPIHT and SVD Techniques
M. Laxmi Prasanna Rani1, G. Sasibhushana Rao2, B. Prabhakara Rao3

1M.Laxmi Prasanna Rani, Department of ECE, MVGR College of Engineering, Vizianagaram (Andhra Pradesh), India.
2G.Sasibhushana Rao, Department of ECE, AU College of Engineering, Andhra University, Visakhapatnam (Andhra Pradesh), India.
3B.Prabhakara Rao, Department of ECE, JNT University, Kakinada (Andhra Pradesh), India.
Manuscript received on 07 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript published on 30 April 2019 | PP: 1129-1133 | Volume-8 Issue-6, April 2019 | Retrieval Number: F3491048619/19©BEIESP
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Abstract: Medical images are generated by different medical imaging techniques require various compression techniques to reduce the storage space with considerable image quality. This paper presents the performance of two different compression techniques for images i.e. Singular Value Decomposition (SVD) using singular values and wavelet transform based progressive structure of Set Partitioning In Hierarchical Trees (SPIHT) to reduce the size of images for accurate diagnosis. These two techniques are practiced on medical images of MRI,CT images of brain and X-ray of hand, and the results of these techniques are compared. SVD with less singular value provides high Compression Ratio (CR) with less Peak Signal to Noise Ratio (PSNR), whereas wavelet based multi resolution SPIHT technique provides more PSNR with better CR. These two techniques are compared with the quality metrics of PSNR, Mean Squared Error (MSE), CR and Bit Per Pixels (BPP). From the results, Wavelet based progressive SPIHT technique provides high PSNR, low MSE with better CR compared to SVD technique.
Keyword: Singular Value Decomposition, Set Partition in Hierarchical Tree, PSNR, MSE.
Scope of the Article: Image analysis and Processing