Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai, Tamil Nadu, 600 119, India.
School of Computer Science Engineering, Mar Ephraem College of Engineering and Technology, Elavuvilai, Kanyakumari, Tamil Nadu, 629171, India.
J Med Syst. 2018 Oct 2;42(11):218. doi: 10.1007/s10916-018-1090-7.
The role of compression is vital in telemedicine for the storage and transmission of medical images. This work is based on Contextual Vector Quantization (CVQ) compression algorithm with codebook optimization by Simulated Annealing (SA) for the compression of CT images. The region of interest (foreground) and background are separated initially by region growing algorithm. The region of interest is encoded with low compression ratio and high bit rate; the background region is encoded with high compression ratio and low bit rate. The codebook generated from foreground and background is merged, optimized by simulated annealing algorithm. The performance of CVQ-SA algorithm was validated in terms of metrics like Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR), the result was superior when compared with classical VQ, CVQ, JPEG lossless and JPEG lossy algorithms. The algorithms are developed in Matlab 2010a and tested on real-time abdomen CT datasets. The quality of reconstructed image was also validated by metrics like Structural Content (SC), Normalized Absolute Error (NAE), Normalized Cross Correlation (NCC) and statistical analysis was performed by Mann Whitney U Test. The outcome of this work will be an aid in the field of telemedicine for the transfer of medical images.
在远程医疗中，压缩在存储和传输医学图像方面起着至关重要的作用。这项工作基于上下文向量量化（CVQ）压缩算法，通过模拟退火（SA）对 CT 图像进行码本优化压缩。首先通过区域生长算法将感兴趣区域（前景）和背景分开。感兴趣区域采用低压缩比和高比特率进行编码；背景区域采用高压缩比和低比特率进行编码。从前景和背景生成的码本通过模拟退火算法进行合并和优化。在 PSNR（峰值信噪比）、MSE（均方误差）和 CR（压缩比）等指标上验证了 CVQ-SA 算法的性能，与经典 VQ、CVQ、JPEG 无损和 JPEG 有损算法相比，结果更优。该算法在 Matlab 2010a 中开发，并在实时腹部 CT 数据集上进行了测试。还通过结构内容（SC）、归一化绝对误差（NAE）、归一化互相关（NCC）等指标验证了重建图像的质量，并通过曼-惠特尼 U 检验进行了统计分析。这项工作的结果将有助于远程医疗领域中医学图像的传输。
