Intelligent reversible watermarking in integer wavelet domain for medical images

Arsalan, Muhammad; Malik, Sana; Khan, Asifullah

doi:10.1016/j.jss.2011.11.005

Cited by 121 publications

(55 citation statements)

References 18 publications

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“…This is accomplished by letting GP develop an optimal expression based on the coefficient magnitude, its location in the block, its neighborhood in the blocks, and the type of sub-band it belongs to. However, at lower payload (0.1-0.3bpp), the proposed IWT-Med shows comparable results to the existing reversible watermarking techniques, except Sachnev et al's technique [20] and GA-RevWM [3]. This is because in GA-RevWM, whereby GA is employed, the threshold selection is block-based, and therefore when the numbers of blocks are few, then GA performs better.…”

Section: Resultsmentioning

confidence: 75%

“…Column (c) shows the dissimilarity between watermarked and the original image, indicating indiscernible embedding of the watermark by using the best evolved expression using GP simulations The naked eye is unable to differentiate this difference, so some amplification is done to enhance the difference as shown in column Fig.7. compares the existing reversible watermarking techniques [36], [37], [28], [31], [18], [3], [35], [27], [25], [19], [8], [12] with the proposed IWT-Med. Tian's technique is quite interesting but its disadvantage is that for locating the expanded differences, extra information of location map is inserted besides the watermark in the image.…”

Section: Resultsmentioning

confidence: 99%

“…Lee et al [18] proposed a genetic algorithm and particle swarm optimization based watermarking technique. Similarly, reversible watermarking is proposed by Arsalan [3], in which Genetic Algorithm evolves optimal/near optimal thresholds for companding. An interesting and effective watermarking technique is proposed by Sachenev et al [25] using sorting and prediction.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

Arsalan

Qureshi

Khan

et al. 2017

Applied Soft Computing

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Rajarajan, M. (2017). Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique. Applied Soft Computing Journal, 51, pp. 1668-179. doi: 10.1016/j.asoc.2016.11.044 This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractThis work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique 'IRW-Med', the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.

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Section: Resultsmentioning

confidence: 75%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

Arsalan

Qureshi

Khan

et al. 2017

Applied Soft Computing

Self Cite

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show abstract

“…In these methods, the quality of ROI is preserved by using reversible methods. Thus, it is not necessary to separate ROI for further protection [1,[5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…With reference to all of the above mentioned methods, except in [1,[5][6][7][8][9][10][11][12][13], prior to starting the watermarking process, ROI needs to be detected and separated from the RONI, and this is considered as a preprocessing step of medical watermarking systems.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking

2015

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This paper presents an automatic region of interest (ROI) segmentation method for application of watermarking in medical images. The advantage of using this scheme is that the proposed method is robust against different attacks such as median, Wiener, Gaussian, and sharpening filters. In other words, this technique can produce the same result for the ROI before and after these attacks. The proposed algorithm consists of three main parts; suggesting an automatic ROI detection system, evaluating the robustness of the proposed system against numerous attacks, and finally recommending an enhancement part to increase the strength of the composed system against different attacks. Results obtained from the proposed method demonstrated the promising performance of the method.

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A new images segmentation method based on modified particle swarm optimization algorithm

Hamdaoui

Ladgham

Sakly

et al. 2013

Int J Imaging Syst Tech

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ABSTRACT:The partitioning of an image into several constituent components is called image segmentation. Many approaches have been developed; one of them is the particle swarm optimization (PSO) algorithm, which is widely used. PSO algorithm is one of the most recent stochastic optimization strategies. In this article, a new efficient technique for the magnetic resonance imaging (MRI) brain images segmentation thematic based on PSO is proposed. The proposed algorithm presents an improved variant of PSO, which is particularly designed for optimal segmentation and it is called modified particle swarm optimization. The fitness function is used to evaluate all the particle swarm in order to arrange them in a descending order. The algorithm is evaluated by performance measures such as run time execution and the quality of the image after segmentation. The performance of the segmentation process is demonstrated by using a defined set of benchmark images and compared against conventional PSO, genetic algorithm, and PSO with Mahalanobis distance based segmentation methods. Then we applied our method on MRI brain image to determinate normal and pathological tissues.

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Intelligent reversible watermarking in integer wavelet domain for medical images

Cited by 121 publications

References 18 publications

Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

A Heuristic Automatic and Robust ROI Detection Method for Medical Image Warermarking

A new images segmentation method based on modified particle swarm optimization algorithm

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