Secure medical image steganography through optimal pixel selection by EH-MB pipelined optimization technique

Abstract Securing the privacy of the medical information through the image steganography process has gained more research interest nowadays to protect the privacy of the patient. In the existing works, least significant bit (LSB) replacement strategy was most popularly used to hide the sensitive contents. Here, every pixel was replaced for achieving higher privacy, but it increased the complexity. This work introduces a novel pixel prediction scheme-based image steganography to overcome the complexity issues prevailing in the existing works. In the proposed pixel prediction scheme, the support vector neural network (SVNN) classifier is utilized for the construction of a prediction map, which identifies the suitable pixels for the embedding process. Then, in the embedding phase, wavelet coefficients are extracted from the medical image based on discrete wavelet transform (DWT) and embedding strength, and the secret message is embedded into the HL wavelet band. Finally, the secret message is extracted from the medical image on applying the DWT. The experimentation of the proposed pixel prediction scheme is done by utilizing the medical images from the BRATS database. The proposed pixel prediction scheme has achieved high performance with the values of 48.558 dB, 0.50009 and 0.9879 for the peak signal to noise ratio (PSNR), Structural Similarity Index (SSIM) and correlation factor, respectively.

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UTILIZATION OF FRACTAL IMAGE MODELS IN MEDICAL IMAGE PROCESSING

Fractals ◽

10.1142/s0218348x94000454 ◽

1994 ◽

Vol 02 (03) ◽

pp. 363-369 ◽

Cited By ~ 7

Author(s):

WALTER S. KUKLINSKI

Keyword(s):

Image Processing ◽

Image Segmentation ◽

Fractal Dimension ◽

Medical Image ◽

Tissue Characterization ◽

Optimization Technique ◽

Medical Image Processing ◽

Fractal Image ◽

Segmentation Process ◽

Image Models

One of the more successful engineering applications of fractal geometry has been the utilization of fractal image models in medical image processing. These applications have included tissue characterization studies, textural image segmentation, and image restoration using fractal constraints. The class of fractal models used in medical image processing and the techniques used to estimate the fractal dimension associated with these models will be reviewed. An image segmentation algorithm that utilized a fractal textural feature and formulated the segmentation process as a configurational optimization problem is presented. The configurational optimization method allows information about both, the degree of correspondence between a candidate segment and an assumed textural model, and morphological information about the candidate segment to be used in the segmentation process. To apply this configurational optimization technique with a fractal textural model however, requires the estimation of the fractal dimension of an irregularly shaped candidate segment. The potential utility of a discrete Gerchberg-Papoulis bandlimited extrapolation algorithm to the estimation of the fractal dimension of an irregularly shaped candidate segment is also discussed.

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Effective payload and improved security using HMT Contourlet transform in medical image steganography

Health and Technology ◽

10.1007/s12553-018-00285-1 ◽

2019 ◽

Vol 10 (1) ◽

pp. 217-229 ◽

Cited By ~ 4

Author(s):

S. Jeevitha ◽

N. Amutha Prabha

Keyword(s):

Medical Image ◽

Image Steganography ◽

Contourlet Transform

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Comparing Yiq and Ycbcr Colour Image Transforms for Semi-Fragile Medical Image Steganography

10.20944/preprints201905.0308.v1 ◽

2019 ◽

Author(s):

Peter Eze ◽

Parampalli Udaya ◽

Robin Evans ◽

Dongxi Liu

Keyword(s):

Visible Light ◽

Spread Spectrum ◽

Medical Image ◽

Medical Images ◽

Ex Vivo ◽

Tamper Detection ◽

Image Steganography ◽

Colour Image ◽

Image Transforms ◽

Spread Spectrum Watermarking

Visible light photography diagnostic images are coloured ex vivo medical images popularly used in Dermatology and Endoscopy for diagnosis and monitoring. The need to protect the integrity of these images as well as associated patient data calls for techniques such as image steganography and watermarking. This research explores and compares the effect of watermarking on the YIQ and YCbCr colour transforms used in processing digital coloured images and video in recent times. Using a new spread spectrum watermarking algorithm, it was found that YIQ has better distortion performance than YCbCr in the order of 3dB while YCbCr had lower BER for accurate watermark retrieval and tamper detection in the order of 1.3 x 10-3.

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A New Optimized Hybrid Local Lifting Wavelet Co-occurrence Texture Pattern for Content Based Medical Image Retrieval

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i11.25351 ◽

2021 ◽

Vol 17 (11) ◽

pp. 157

Author(s):

R Varaprasada Rao ◽

T Jaya Chandra Prasad

Keyword(s):

Image Retrieval ◽

Medical Image ◽

Optimization Technique ◽

Histogram Equalization ◽

Directional Pattern ◽

Query Image ◽

Medical Image Retrieval ◽

Texture Pattern ◽

Feature Descriptors ◽

Lifting Wavelet

<p class="0abstract">Medical image retrieval (MIR) is a hard task owing to the varied patterns and structures in the medical images. The feature descriptors have been used to describe the images in most MIR approaches. Based on the local relationship, several feature descriptors of neighbouring image pixels have been proposed for MIR so far, but their low performance scores make them unsuitable. In this paper, an efficient optimized hybrid local lifting wavelet co-occurrence texture pattern for content-based MIR is proposed. Initially, image resize and Adaptive histogram equalization technique is used to carried out for contrast enhancement. Then Local Lifting Wavelet Co-occurrence Texture Pattern is derived using Local tetra pattern, Gradient directional pattern, lifting wavelet transform and Gray level co-occurrence matrix. An Equilibrium optimization technique is employed to select the most important features of an image from the obtained feature vectors (FV). Finally, to match the query image with the database images, distance between their FV is computed and the minimum distance images are considered as retrieval outcome. Three benchmark medical databases of various modalities (CT and MRI) are used to test the efficiency of the proposed method: EXACT-09, TCIA-CT, and OASIS. The experimental results prove that the proposed approach outperforms existing descriptors in terms of APR and ARR.</p>

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A novel approach to jointly address localization and classification of breast cancer using bio-inspired approach

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i2.pp992-1001 ◽

2019 ◽

Vol 9 (2) ◽

pp. 992

Author(s):

Sushma S. J. ◽

S. C. Prasanna Kumar

Keyword(s):

Breast Cancer ◽

Medical Image ◽

Optimization Technique ◽

Generalized Solution ◽

Image Process ◽

Classification Problems ◽

Novel Approach ◽

Research Gap

Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process.

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