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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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基于机器学习的多用途医疗图像水印.

Rishi Sinhal¹, Irshad Ahmad Ansari¹

¹Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005 India.

Neural computing & applications

|June 26, 2023

概括

此摘要是机器生成的。

这项研究引入了一种使用深度神经网络的安全医疗图像水印方法. 它确保数据完整性,并允许敏感区域的完整恢复,增强医疗数据的安全性.

关键词:

图像身份验证图像身份验证医疗图像水印的使用多用途的水印.验证所有权验证所有权验证投资回报率 (ROI) 的可逆性

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科学领域:

计算机科学计算机科学
医疗成像医学成像
密码学密码学密码学密码学

背景情况:

数字数据的安全性至关重要,特别是对于敏感的医疗图像.
未经授权的访问和操纵医疗数据可能导致诊断错误和伤害患者.
现有的安全措施可能缺乏医疗图像应用的稳定性或可逆性.

研究的目的:

使用深度神经网络开发一个盲人和强大的医疗图像水印框架.
确保医疗图像在共享和存储期间的完整性和机密性.
提供一种可逆方法来恢复感兴趣地区 (ROI) 数据.

主要方法:

一个新的水标框架,结合了LZW压缩,整数波形变换 (IWT) 和SHA-256哈希键.
在原始图像中嵌入一个强大的水标,并在无兴趣区域 (RONI) 中嵌入一个脆弱的水标 (包含ROI恢复数据和哈希键).
利用深度神经网络进行高效和强大的水印提取和认证.

主要成果:

拟议的方案显示了显著的不可察觉性和强大的水印提取.
实现了正确的身份验证和完全可逆的ROI恢复性质.
与现有的水印系统相比,模拟结果显示性能优越.

结论:

开发的基于深度神经网络的水标框架为医疗图像提供了有效的安全解决方案.
该方法确保了数据完整性,机密性和完全可逆性,这对于医疗应用至关重要.
这种方法显著提高了医疗图像数据管理的安全性和可靠性.