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Multiwavelet Transform in Compression of Medical Images


Affiliations
1 Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology, India
     

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This paper analyses performance of multiwavelets - a variant of wavelet transform on compression of medical images. To do so, two processes namely, transformation for decorrelation and encoding are done. In transformation stage medical images are subjected to multiwavelet transform using multiwavelets such as Geronimo- Hardin-Massopust, Chui Lian, Cardinal 2 Balanced (Cardbal2) and orthogonal symmetric/antsymmetric multiwavelet (SA4). Set partitioned Embedded Block Coder is used as a common platform for encoding the transformed coefficients. Peak Signal to noise ratio, bit rate and Structural Similarity Index are used as metrics for performance analysis. For experiment we have used various medical images such as Magnetic Resonance Image, Computed Tomography and X-ray images.

Keywords

Wavelets, Multiwavelets, Medical Image and SPECK.
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  • Multiwavelet Transform in Compression of Medical Images

Abstract Views: 250  |  PDF Views: 0

Authors

V. K. Sudha
Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology, India
R. Sudhakar
Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology, India

Abstract


This paper analyses performance of multiwavelets - a variant of wavelet transform on compression of medical images. To do so, two processes namely, transformation for decorrelation and encoding are done. In transformation stage medical images are subjected to multiwavelet transform using multiwavelets such as Geronimo- Hardin-Massopust, Chui Lian, Cardinal 2 Balanced (Cardbal2) and orthogonal symmetric/antsymmetric multiwavelet (SA4). Set partitioned Embedded Block Coder is used as a common platform for encoding the transformed coefficients. Peak Signal to noise ratio, bit rate and Structural Similarity Index are used as metrics for performance analysis. For experiment we have used various medical images such as Magnetic Resonance Image, Computed Tomography and X-ray images.

Keywords


Wavelets, Multiwavelets, Medical Image and SPECK.