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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大个应助tt采纳,获得10
刚刚
Yoh1220发布了新的文献求助30
刚刚
2秒前
知画春秋完成签到,获得积分10
2秒前
3秒前
3秒前
4秒前
大气石头完成签到,获得积分10
5秒前
李健应助开心夏真采纳,获得10
5秒前
5秒前
JJ完成签到,获得积分10
6秒前
6秒前
星辰大海应助多情小熊猫采纳,获得10
6秒前
6秒前
Mr.Su发布了新的文献求助10
6秒前
Jasper应助汤飞柏采纳,获得10
7秒前
阿姜姜姜姜完成签到,获得积分10
7秒前
7秒前
7秒前
夜月残阳完成签到,获得积分10
7秒前
琪七完成签到,获得积分10
8秒前
manman完成签到,获得积分10
8秒前
缓慢安阳完成签到,获得积分20
8秒前
9秒前
lqkcqmu发布了新的文献求助30
9秒前
单薄的夜南应助11采纳,获得10
9秒前
时笙发布了新的文献求助10
9秒前
KM比比发布了新的文献求助10
10秒前
10秒前
科研渣渣发布了新的文献求助10
10秒前
材料小学生完成签到,获得积分10
10秒前
10秒前
自由山槐完成签到,获得积分10
11秒前
Terencecx完成签到,获得积分10
11秒前
所所应助哈哈哈哈哈采纳,获得10
11秒前
清晾油完成签到,获得积分10
11秒前
李乐平完成签到,获得积分10
11秒前
YZ完成签到,获得积分10
12秒前
besatified应助JJ采纳,获得30
12秒前
LQ发布了新的文献求助10
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970572
求助须知:如何正确求助?哪些是违规求助? 3515219
关于积分的说明 11177438
捐赠科研通 3250374
什么是DOI,文献DOI怎么找? 1795265
邀请新用户注册赠送积分活动 875750
科研通“疑难数据库(出版商)”最低求助积分说明 805054