平滑的轮状Texel四边形网络用于3D视网膜OCT图像压缩
N Nanthini1, S Sasipriya2, M Ramkumar3
1Department of Electronics and Communication Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu, India.
Journal of clinical ultrasound : JCU
|January 26, 2026
概括
一个新的SGTQN (Smooth Gyrated Texel Quadrivium Network) 通过处理信号依赖和混合的低对比度剩余噪声 (HLRN) 来有效地消除视网膜光学连贯断层扫描图像. 这种方法提高了图像压缩和细分质量.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 视网膜光连贯断层扫描 (OCT) 图像压缩需要有效的预处理以减少噪声.
- 现有的无声化方法与信号依赖噪声和混合的低对比度余噪声 (HLRN) 斗争.
- 当前的细分技术往往忽视视网膜神经的偏差,影响图像的解释性.
研究的目的:
- 提出一个新型网络,即SGTQN (Smooth Gyrated Texel Quadrivium Network),用于OCT先进的图像染和信息收集.
- 开发一种改进的细分方法,将视网膜神经偏差纳入自我解释的图像.
- 通过统一的压缩技术来提高OCT图像压缩质量.
主要方法:
- 该SGTQN使用添加式Ascombe光滑选器将Poisson噪声转换为高斯噪声并删除HLRN.
- 为了细分,考虑偏差值,采用了一种带有标准化螺旋层的即兴螺旋Alexa网.
- 一个Texel四边形卷积网络将聚合层修改为Texel四边形层,以实现统一的压缩.
主要成果:
- 拟议的模型有效地减少了OCT图像中的信号依赖和HLRN.
- 细分方法通过结合视网膜神经偏差来产生自我解释的图像.
- 高质量的图像压缩是通过统一的压缩和辅助矢量坐标实现的.
- 该模型表现出高精度95%和低平均平方误差0.02.
结论:
- SGTQN提供了一个强大的解决方案,用于消除和增强视网膜OCT图像中的信息提取.
- 综合细分方法通过考虑神经偏差来提高图像的解释性.
- 这种新型压缩技术产生了高分辨率,高质量的压缩图像,进步了OCT分析.
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