Medical Image Compression Using Vector Quantization and Gaussian Mixture Model. | ||||
| MEJ- Mansoura Engineering Journal | ||||
| Article 2, Volume 28, Issue 3, September 2003, Page 13-21 PDF (203.65 K) | ||||
| Document Type: Research Studies | ||||
| DOI: 10.21608/bfemu.2021.141417 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Alaa M. Elsayad 
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| Computers and Systems Dept., Electronics Research Institute | ||||
| Abstract | ||||
| Codebook design for vector quantization could be performed using clustering technique. The Gaussian Mixture Modeling (GMM) clustering algorithm involves modeling a statistical distribution by a mixture (or weighted sum) of other distributions. GMM has proven superior efficiency in both time and accuracy and has been used with vector quantization in some applications. This paper introduces a medical image compression technique using GMM clustering algorithm and vector quantization. The parameters of each Gaussian component are estimated using the Expectation Maximization iterative method to minimize the error function (maximize the Likelihood). The results for the proposed compression technique are compared with those obtained using the well-known Kohonen SOM neural network compression technique. | ||||
| Keywords | ||||
| Clustering; Vector Quantization; and Image Compression | ||||
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