An efficient medical image compression technique for telemedicine systems

远程医疗 计算机科学 图像压缩 图像(数学) 计算机视觉 压缩(物理) 人工智能 图像处理 医疗保健 材料科学 复合材料 经济增长 经济
作者
R. Monika,Samiappan Dhanalakshmi
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:80: 104404-104404 被引量:12
标识
DOI:10.1016/j.bspc.2022.104404
摘要

The medical practitioners primarily used medical images to reveal abnormalities in the internal critical organs and structures of body covered by the bones and the skin. Main application of medical imaging is to perform medical diagnosis from the image features extracted. Processing these images are very much required for assessing the patient’s condition. However, long-term monitoring of the patient using certain medical imaging technologies produces enormous volumes of data everyday. There is a need to compress the data to reduce redundancies and speed up the acquisition process, making them suitable for efficient transmission and analysis. Recently Compressed Sensing (CS) has been widely used for image compression at high speed with fewer samples. High-quality reconstruction using conventional CS and Block based CS (BCS) is a matter of utmost concern as they follow the random selection of samples. This could be overcome by adaptively selecting samples from various image regions using Adaptive Block Compressed Sensing (ABCS). This paper proposes Coefficient Mixed Thresholding based ABCS (CMT-ABCS) for compressing different medical images with a high compression ratio. The experimental outcomes exhibit a noteworthy improvement in the proposed method’s performance metrics when compared to other state-of-the-art approaches. There is a increase in PSNR of 5–10 dB, SSIM of 0.1–0.2 with NCC values closer to 1 and NAE values closer to 0. At low sampling rate, the reconstruction was greatly enhanced with only around 10% measurements/samples. • Reconstructs entire image with only 10% of samples. • Coefficient mixed thresholding involves simple calculation procedures. • Achieves 40%–70% of compression. • Significant improvement in image quality measures like PSNR, SSIM, NCC and NAE. • Blocking artifacts and improper block reconstruction are eliminated completely. • Remarkable quality improvement is noticed even at low sampling rate.
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