基于纹理度划分的医学图像可逆信息隐藏方法

doi:10.11772/j.issn.1001-9081.2017122885

计算机应用 ›› 2018, Vol. 38 ›› Issue (8): 2293-2300.DOI: 10.11772/j.issn.1001-9081.2017122885

基于纹理度划分的医学图像可逆信息隐藏方法

才雪, 杨杨, 肖星星

安徽大学电子信息工程学院, 合肥 230039

收稿日期:2017-12-11 修回日期:2018-01-26 出版日期:2018-08-10 发布日期:2018-08-11
通讯作者: 杨杨
作者简介:才雪(1993-),女,山东济宁人,硕士研究生,主要研究方向:可逆信息隐藏、图像质量评价;杨杨(1980-),女,安徽合肥人,副教授,博士,主要研究方向:可逆信息隐藏、图像质量评价;肖星星(1994-),女,山东聊城人,硕士研究生,主要研究方向:加密域的可逆信息隐藏。
基金资助:
国家自然科学基金资助项目（61502007）；安徽自然科学基金面上资助项目（1608085MF125）；中国博士后科学基金资助项目（2015M582015）；安徽大学博士科研启动基金资助项目（J01001319）；安徽大学骨干教师培养基金资助项目。

Reversible data hiding method based on texture partition for medical images

CAI Xue, YANG Yang, XIAO Xingxing

School of Electronics and Information Engineering, Anhui University, Hefei Anhui 230039, China

Received:2017-12-11 Revised:2018-01-26 Online:2018-08-10 Published:2018-08-11
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61502007), the Natural Science Foundation of Anhui Province (1608085MF125), the China Postdoctoral Science Foundation (2015M582015), the Doctoral Scientific Research Foundation of Anhui University (J01001319), the Backbone Teacher Training Program of Anhui University.

摘要/Abstract

摘要： 针对目前具有对比度增强效果的可逆信息隐藏（RDH）算法大多受到嵌入率的影响，未能实现较好的对比度增强效果的问题，提出一种基于纹理度划分的医学图像可逆信息隐藏方法。首先，通过对比度拉伸的方法实现医学图像对比度增强；然后，再利用医学图像纹理度的自身特点，将医学图像划分为高、低两类纹理度等级，其中高纹理度等级构成医学图像的关键部分；最后，为了进一步增强高纹理度等级的对比度并保证信息嵌入率，对不同的纹理度等级像素采用不同的嵌入方法。为了与其他具有对比度增强效果的医学图像可逆信息隐藏算法比较载密图像的对比度增强效果，采用了针对对比度失真的无参考图像质量评价标准（NR-CDIQA）作为实验客观依据。实验结果表明，在不同嵌入率下，使用所提方法的载密图像的NR-CDIQA值更高，对比度增强效果更好。

关键词: 医学图像, 可逆信息隐藏, 对比度增强, 高嵌入率, 纹理度

Abstract: To solve the problem that contrast enhancement effect is affected by the embedding rate in most existing Reversible Data Hiding (RDH) algorithms, a new RDH method based on texture partition for medical images was proposed. Firstly, the contrast of an image was stretched to enhance image contrast, and then according to the characteristics of medical image texture, the medical image was divided into high and low texture levels. The key partion of the medical image mainly had high texture level. To enhance the contrast of high texture level further and guarantee the infomation embedding capacity, different embedding processes were adopted for high and low texture levels. In order to compare the effect of contrast enhancement between the proposed method and other RDH algorithms for medical images, No-Reference Contrast-Distorted Images Quality Assessment (NR-CDIQA) was adopted as the evaluation standards. The experimental results show that the marked images processed by the proposed method can get better NR-CDIQA and contrst enhancement in different embedding rate.

Key words: medical image, Reversible Data Hiding (RDH), contrast enhancement, high embedding rate, texture degree

中图分类号:

才雪, 杨杨, 肖星星. 基于纹理度划分的医学图像可逆信息隐藏方法[J]. 计算机应用, 2018, 38(8): 2293-2300.

CAI Xue, YANG Yang, XIAO Xingxing. Reversible data hiding method based on texture partition for medical images[J]. Journal of Computer Applications, 2018, 38(8): 2293-2300.

参考文献

[1] TIAN J. Reversible data embedding using a difference expansion[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2003, 13(8):890-896.
[2] 张弢,康缘,任帅,等.基于压缩感知和GHM多小波变换的信息隐藏算法[J].计算机应用,2017,37(9):2581-2584.(ZHANG T, KANG Y, REN S, et al. Information hiding algorithm based on compression sensing and GHM multiwavelet transform[J]. Journal of Computer Applications, 2017, 37(9):2581-2584.)
[3] ZHAO Z, LUO H, LU Z M, et al. Reversible data hiding based on multilevel histogram modification and sequential recovery[J]. AEU - International Journal of Electronics and Communications, 2011, 65(10):814-826.
[4] YANG Y, SUN X, YANG H, et al. A contrast-sensitive reversible visible image watermarking technique[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2009, 19(5):656-667.
[5] 陈甬娜,周宇,王晓东,等.基于菱形编码的视频信息隐藏算法[J].计算机应用,2017,37(10):2806-2812.(CHEN Y N, ZHOU Y, WANG X D, et al. Information hiding algorithm for HEVC based on diamond coding[J]. Journal of Computer Applications, 2017, 37(10):2806-2812.)
[6] 任帅,张弢,杨涛,等.基于三维模型球型分割的信息隐藏算法[J].计算机应用,2017,37(9):2576-2580.(REN S, ZHANG T, YANG T, et al. Information hiding algorithm based on spherical segmentation of 3D model[J]. Journal of Computer Applications, 2017, 37(9):2576-2580.)
[7] WU H T, DUGELAY J L, SHI Y Q. Reversible image data hiding with contrast enhancement[J]. IEEE Signal Processing Letters, 2014, 22(1):81-85.
[8] GAO G, SHI Y Q. Reversible data hiding using controlled contrast enhancement and integer wavelet transform[J]. IEEE Signal Processing Letters, 2015, 22(11):2078-2082.
[9] YANG Y, ZHANG W, LIANG D, et al. Reversible data hiding in medical images with enhanced contrast in texture area[J]. Digital Signal Processing, 2016, 52(C):13-24.
[10] SACHNEV V, KIM H J, NAM J, et al. Reversible watermarking algorithm using sorting and prediction[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2009, 19(7):989-999.
[11] National Cancer Institute. National biomedical imaging archive[EB/OL].[2017-06-20]. http://imaging.nci.nih.gov.
[12] HOWARD P G, KOSSENTINI F, MARTINS B, et al. The emerging JBIG2 standard[J]. IEEE Transactions on Circuits & Systems for Video Technology, 1998, 8(7):838-848.
[13] YANG Y, ZHANG W, LIANG D, et al. A ROI-based high capacity reversible data hiding scheme with contrast enhancement for medical images[J]. Multimedia Tools & Applications, 2017, 77(14):18043-18065.
[14] FANG Y, MA K, WANG Z, et al. No-reference quality assessment of contrast-distorted images based on natural scene statistics[J]. IEEE Signal Processing Letters, 2014, 22(7):838-842.
[15] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4):600-612.

基于纹理度划分的医学图像可逆信息隐藏方法

Reversible data hiding method based on texture partition for medical images

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