Optimised blind image watermarking method based on firefly algorithm in DWT‐QR transform domain

Guo, Yong; Li, Bing‐Zhao; Goel, Naveen Krishan

doi:10.1049/iet-ipr.2016.0515

Cited by 68 publications

(38 citation statements)

References 36 publications

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“…After DWT decomposition at the 2nd level is performed on the ROI, the approximation at the 2nd level is obtained to combine the original image for the segment of the tumor. Figure 6 shows the wavelet approximation and details in the horizontal, vertical, and diagonal directions at the 1st and 2nd levels of wavelet decomposition [15].…”

Section: Methodsmentioning

confidence: 99%

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Yuan

Liu

2020

Computational and Mathematical Methods in Medicine

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Purpose. The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC). Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic. Methods. Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and K-means algorithm is proposed. Secondly, TA was performed and the Gabor wavelet analysis is used to extract the texture feature of an MRI tumor. Then, according to the distance relationship between the features, key features are sorted and feature subsets are selected. Finally, the feature subset is classified by using a support vector machine and adjusted parameters to achieve the best classification effect. Results. By selecting key features for classification prediction, the classification accuracy of the classification model can reach 81.33%. 3-, 4-, and 5-fold cross-validation of the prediction accuracy of the support vector machine model is 77.79%~81.94%. Conclusion. The pathological grading of IDC can be predicted and evaluated by texture analysis and feature extraction of breast tumors. This method can provide much valuable information for doctors’ clinical diagnosis. With further development, the model demonstrates high potential for practical clinical use.

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Section: Methodsmentioning

confidence: 99%

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Yuan

Liu

2020

Computational and Mathematical Methods in Medicine

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“…Drosophila optimization algorithm. Drosophila optimization algorithm of digital image watermarking algorithm is an adaptive image digital watermarking algorithm, the algorithm determines the type of digital watermark used in the digital watermarking algorithm and the digital watermark preprocessing, and the digital watermark embedding rules and initial steps are extracted and determined [6,19]. Drosophila optimization algorithm is a new method based on Drosophila foraging behavior to find global optimum.…”

Section: Image Digital Watermarking Optimization Algorithm Based Onmentioning

confidence: 99%

Research on image digital watermarking optimization algorithm under virtual reality technology

Zhang¹,

Ma²

2019

Discrete &Amp; Continuous Dynamical Systems - S

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Aiming at the shortcomings of current algorithms due to the fixed steps, which is easy to fall into local optimum, with robustness and poor transparency, and cannot be balanced against various common attacks, an optimization algorithm of digital image watermarking algorithm based on Drosophila was proposed. In the support of the virtual reality technology, the original color host image was transformed from the RGB space to YCrCb space, and the pixel block of the Y component was divided into a certain size; according to the principle of forming DC coefficients in the DCT domain, the DC coefficient of each block is calculated directly in the airspace, and the amount of modification for each DC coefficient is determined based on the watermark information and the quantization step size; according to the distribution characteristics of DC coefficient, watermarks are embedded directly in the airspace; the type of digital watermarking and digital watermarking pretreatment methods were determined by using Drosophila optimization algorithm. At the same time, digital watermark embedding, extraction rules and initial steps were selected and identified. The Drosophila optimization algorithm with step size reduces the balance between global and local search ability, which makes up for the shortcomings of traditional algorithm. The experimental results showed that the proposed algorithm can effectively balance the invisibility and robustness of the watermark, and can resist all kinds of common attacks, which with a better visual extraction effect. 2010 Mathematics Subject Classification. 60J67.

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“…Replace h 1,1 with h ' 1,1 and then apply the inverse of Hessenberg decomposition to get the watermarked block A ' . (13) Step 11. Repeat the embedding steps 6-10 until all watermark information is embedded.…”

Section: The Process Of Watermark Embeddingmentioning

confidence: 99%

Colour Image Blind Watermarking Scheme Based on Fast Walsh Hadamard Transform and Hessenberg Decomposition

Abodena¹,

Agoyi²

2018

STUD INFORM CONTROL

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Colour image watermarking has become one of the most important algorithms for copyright protection. The following paper will present an innovative scheme for watermarking blind colour images using the discrete wavelet transform (DWT), fast Walsh Hadamard transform (FWHT) and the Hessenberg decomposition as its basis. First, two-level DWT followed by FWHT are used to decompose the host image's red channel. Next, the FWHT coefficients are split into 4 × 4 non-overlapping blocks. Then, each selected block is decomposed using Hessenberg decomposition, where the first row, first column element of the upper Hessenberg matrix H is quantified to embed the watermark information. Peak signalto-noise ratio, normalized cross-correlation and structural similarity index measure are used to evaluate the feasibility and the robustness. The experimental results have demonstrated that the proposed watermarking scheme is highly invisible with PSNR > 40 dB, for several watermarked colour images, with a capacity of 4096 bits and execution time of 0.7415s. The proposed watermarking scheme is also highly resistant to both common image processing and geometrical attacks such as filtering, JPEG2000, noise adding, cropping, scaling, blurring and sharpening, and others.

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Optimised blind image watermarking method based on firefly algorithm in DWT‐QR transform domain

Cited by 68 publications

References 36 publications

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI

Research on image digital watermarking optimization algorithm under virtual reality technology

Colour Image Blind Watermarking Scheme Based on Fast Walsh Hadamard Transform and Hessenberg Decomposition

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