It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a...It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a content-based image hashing scheme using wave atoms is proposed, which satisfies the above criteria. Compared with traditional transforms like wavelet transform and discrete cosine transform （DCT）, wave atom transform is adopted for the sparser expansion and better characteristics of texture feature extraction which shows better performance in both robustness and fragility. In addition, multi-frequency detection is presented to provide an application-defined trade-off. To ensure the security of the proposed approach and its resistance to a chosen-plaintext attack, a randomized pixel modulation based on the Rdnyi chaotic map is employed, combining with the nonliner wave atom transform. The experimental results reveal that the proposed scheme is robust against content-preserving manipulations and has a good discriminative capability to malicions tampering.展开更多
目的:提出一种基于波原子变换的超声图像纹理特征分类方法,并验证其分类效果。方法:首先选取肝硬化超声图像,运用波原子变换提取感兴趣区域的图像纹理特征,然后利用奇异值分解(singular value decomposition,SVD)寻找最佳特征组合,最后...目的:提出一种基于波原子变换的超声图像纹理特征分类方法,并验证其分类效果。方法:首先选取肝硬化超声图像,运用波原子变换提取感兴趣区域的图像纹理特征,然后利用奇异值分解(singular value decomposition,SVD)寻找最佳特征组合,最后运用支持向量机(support vector machine,SVM)对样本图像进行分类。结果:实验结果表明该方法区分正常肝脏与肝硬化组织、血管和远场阴影有较高的分类率,其准确度分别为77.58%、92.74%和63.55%。结论:波原子变换作为一种新的多尺度图像分析工具,用来提取和表征医学超声图像纹理特征具有显著的效果,可用于手术指导和超声设备的辅助诊断。展开更多
文摘It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a content-based image hashing scheme using wave atoms is proposed, which satisfies the above criteria. Compared with traditional transforms like wavelet transform and discrete cosine transform （DCT）, wave atom transform is adopted for the sparser expansion and better characteristics of texture feature extraction which shows better performance in both robustness and fragility. In addition, multi-frequency detection is presented to provide an application-defined trade-off. To ensure the security of the proposed approach and its resistance to a chosen-plaintext attack, a randomized pixel modulation based on the Rdnyi chaotic map is employed, combining with the nonliner wave atom transform. The experimental results reveal that the proposed scheme is robust against content-preserving manipulations and has a good discriminative capability to malicions tampering.
文摘目的:提出一种基于波原子变换的超声图像纹理特征分类方法,并验证其分类效果。方法:首先选取肝硬化超声图像,运用波原子变换提取感兴趣区域的图像纹理特征,然后利用奇异值分解(singular value decomposition,SVD)寻找最佳特征组合,最后运用支持向量机(support vector machine,SVM)对样本图像进行分类。结果:实验结果表明该方法区分正常肝脏与肝硬化组织、血管和远场阴影有较高的分类率,其准确度分别为77.58%、92.74%和63.55%。结论:波原子变换作为一种新的多尺度图像分析工具,用来提取和表征医学超声图像纹理特征具有显著的效果,可用于手术指导和超声设备的辅助诊断。
