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Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application
S N Kumar1, A Lenin Fred2, P Sebastin Varghese3
1Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai, Tamil Nadu, 600 119, India. appu123kumar@gmail.com.
Journal of Medical Systems
|October 4, 2018
Summary
This study introduces an optimized Contextual Vector Quantization (CVQ) algorithm using Simulated Annealing (SA) for superior CT image compression in telemedicine. The novel CVQ-SA method enhances medical image storage and transmission efficiency.
Area of Science:
- Medical Imaging
- Data Compression
- Telemedicine
Background:
- Medical image compression is crucial for telemedicine.
- Existing methods face challenges in balancing quality and efficiency.
Purpose of the Study:
- To develop and validate an optimized compression algorithm for CT images.
- To improve the efficiency of medical image storage and transmission in telemedicine.
Main Methods:
- Contextual Vector Quantization (CVQ) algorithm with Simulated Annealing (SA) optimization.
- Region growing for separating regions of interest (foreground) and background.
- Differential encoding rates for foreground and background regions.
- Codebook optimization using Simulated Annealing.
Main Results:
- The CVQ-SA algorithm demonstrated superior performance compared to VQ, CVQ, JPEG lossless, and JPEG lossy algorithms.
- Validation metrics included Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Compression Ratio (CR).
- Reconstructed image quality was assessed using Structural Content (SC), Normalized Absolute Error (NAE), and Normalized Cross Correlation (NCC).
Conclusions:
- The developed CVQ-SA algorithm offers an effective solution for CT image compression in telemedicine.
- This method aids in efficient storage and transmission of medical images, benefiting telemedicine applications.
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