Embedding information reversibly in medical images for e-health

2020 ◽  
Vol 39 (6) ◽  
pp. 8389-8398
Author(s):  
Asra Kamili ◽  
Izat Fatima ◽  
Muzamil Hassan ◽  
Shabir A. Parah ◽  
V. Vijaya Kumar ◽  
...  
Keyword(s):  
Medical Images ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Embedding Capacity ◽  
Electronic Patient Records ◽  
Proper Diagnosis ◽  
High Embedding Capacity ◽  
Embedding Methods ◽  
Zero Points

Embedding information in medical images is considered as one of the significant methods for safeguarding the integrity and authenticity of medical images besides providing security to electronic patient records (EPR). The conventional embedding methods deteriorate the perceptual quality of medical images making them unsuitable for proper diagnosis. To preserve the perceptual quality of medical images reversible embedding is used. The reversible embedding schemes, however, have less embedding capacity. In this work, a reversible scheme based on histogram bin shifting and RGB plane concatenation has been proposed which offers high embedding capacity as well. We have exploited the fact that medical images, unlike general images, consist of a large number of peaks and zero points that can be employed for reversibly embedding the data. Reversibility ensures that original image restoration takes place after the extraction of embedded data, which is of great importance in medical images for proper diagnosis and treatment. We have used various subjective and objective image quality metrics for analyzing the scheme. The proposed scheme has been shown to provide a Peak Signal to Noise Ratio (PSNR) value of above 56 dB for an embedding capacity of 0.58 bits per pixel (bpp). The results obtained show that the performance of scheme presented is far better in comparison to the state-of-the-art.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

Analysis of the impact of measurement matrices in compressive sensing for medical images

2020 ◽  
pp. 591-600
Author(s):  
G. Kowsalya ◽  
H. A. Christinal ◽  
D. A. Chandy ◽  
S. Jebasingh ◽  
C. Bajaj
Keyword(s):  
Compressive Sensing ◽  
Sparse Representation ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Basis Pursuit ◽  
Measurement Matrix ◽  
Compressed Images ◽  
The Impact ◽  
Reconstruction Time

Compressive sensing of images is based on three key components namely sparse representation, construction of measurement matrix and reconstruction of images. The visual quality of reconstructed image is prime important in medical images. We apply Discrete Cosine Transform (DCT) for sparse representation of medical images. This paper focuses on the analysis of measurement matrices on compressive sensing of MRI images. In this work, the Gaussian and Bernoulli type of random matrices are considered as measurement matrix. The compressed images are reconstructed using Basis Pursuit algorithm. Peak-signal-to noise ratio and reconstruction time are the metrics taken for evaluating the performance of measurement matrices towards compressive sensing of medical images.

scholarly journals High-Capacity Image Steganography with Minimum Modified Bits Based on Data Mapping and LSB Substitution

2018 ◽  
Vol 8 (11) ◽  
pp. 2199 ◽  
Author(s):  
Abdul Zakaria ◽  
Mehdi Hussain ◽  
Ainuddin Wahab ◽  
Mohd Idris ◽  
Norli Abdullah ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
High Capacity ◽  
Visual Quality ◽  
Data Mapping ◽  
Embedding Capacity ◽  
Secret Data ◽  
Stego Image ◽  
Mapping Strategy ◽  
Lsb Substitution

Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.

Block-Based High Capacity Multilevel Image Steganography

2016 ◽  
Vol 25 (08) ◽  
pp. 1650091 ◽  
Author(s):  
Geeta Kasana ◽  
Kulbir Singh ◽  
Satvinder Singh Bhatia
Keyword(s):  
Digital Images ◽  
High Capacity ◽  
Block Size ◽  
Visual Quality ◽  
Cover Image ◽  
Embedding Capacity ◽  
Secret Data ◽  
Block Based ◽  
High Embedding Capacity

This paper proposes a block-based high capacity steganography technique for digital images. The cover image is decomposed into blocks of equal size and the largest pixel of each block is found to embed the secret data bits and also the smallest pixel of each block is used for embedding to enhance the capacity. Embedding of secret data is performed using the concept that the pixel of a cover image has only two states — even and odd. Multilevel approach is also combined in the proposed technique to achieve high embedding capacity. In order to make the proposed technique more secure, a key is generated using embedding levels, block size, pixel embedding way, encryption parameters, and starting blocks of each embedding levels. Embedding capacity and visual quality of stego images generated by the proposed steganography technique are higher than the existing techniques. Steganalysis tests have been performed to show the un-detectability and imperceptibility of the proposed technique.

scholarly journals The Type of Noise Influences Quality Ratings for Noisy Speech in Hearing Aid Users

2020 ◽  
Vol 63 (12) ◽  
pp. 4300-4313
Author(s):  
Emily M. H. Lundberg ◽  
Song Hui Chon ◽  
James M. Kates ◽  
Melinda C. Anderson ◽  
Kathryn H. Arehart
Keyword(s):  
Fast Food ◽  
Signal To Noise Ratio ◽  
Hearing Aid ◽  
Sound Quality ◽  
Perceptual Quality ◽  
Lower Envelope ◽  
Quality Ratings ◽  
Noise Type ◽  
Street Traffic

Purpose The overall goal of the current study was to determine whether noise type plays a role in perceptual quality ratings. We compared quality ratings using various noise types and signal-to-noise ratio (SNR) ranges using hearing aid simulations to consider the effects of hearing aid processing features. Method Ten older adults with bilateral mild to moderately severe sensorineural hearing loss rated the sound quality of sentences processed through a hearing aid simulation and presented in the presence of five different noise types (six-talker babble, three-talker conversation, street traffic, kitchen, and fast-food restaurant) at four SNRs (3, 8, 12, and 20 dB). Results Everyday noise types differentially affected sound quality ratings even when presented at the same SNR: Kitchen and three-talker noises were rated significantly higher than restaurant, traffic, and multitalker babble, which were not different from each other. The effects of noise type were most pronounced at poorer SNRs. Conclusions The findings of this study showed that noise types differentially affected sound quality ratings. The differences we observed were consistent with the acoustic characteristics of the noise types. Noise types having lower envelope fluctuations yielded lower quality ratings than noise types characterized by sporadic high-intensity events at the same SNR.

scholarly journals A robust watermarking algorithm for medical images

2020 ◽  
Vol 20 (3) ◽  
pp. 1601
Author(s):  
Ahmed Nagm ◽  
Mohammed Safy
Keyword(s):  
Medical Images ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Absolute Error ◽  
Patient Privacy ◽  
Secret Code ◽  
The Mean ◽  
Image Quality Index

<p>Integrated healthcare systems require the transmission of medical images between medical centres. The presence of watermarks in such images has become important for patient privacy protection. However, some important issues should be considered while watermarking an image. Among these issues, the watermark should be robust against attacks and does not affect the quality of the image. In this paper, a watermarking approach employing a robust dynamic secret code is proposed. This approach is to process every pixel of the digital image and not only the pixels of the regions of non-interest at the same time it preserves the image details. The performance of the proposed approach is evaluated using several performance measures such as the Mean Square Error (MSE), the Mean Absolute Error (MAE), the Peak Signal to Noise Ratio (PSNR), the Universal Image Quality Index (UIQI) and the Structural Similarity Index (SSIM). The proposed approach has been tested and shown robustness in detecting the intentional attacks that change image, specifically the most important diagnostic information.</p>

scholarly journals Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Suneet Gupta ◽  
Rabins Porwal
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
Brain Cancer ◽  
Medical Images ◽  
Subsequent Treatment ◽  
Imaging Systems ◽  
Medical Professionals ◽  
Quantitative Metrics ◽  
Proper Diagnosis

Medical imaging systems often produce images that require enhancement, such as improving the image contrast as they are poor in contrast. Therefore, they must be enhanced before they are examined by medical professionals. This is necessary for proper diagnosis and subsequent treatment. We do have various enhancement algorithms which enhance the medical images to different extents. We also have various quantitative metrics or measures which evaluate the quality of an image. This paper suggests the most appropriate measures for two of the medical images, namely, brain cancer images and breast cancer images.

scholarly journals Enhanced Medical Image Watermarking Scheme with CLA-HE& DWT, SVD Transforms

2018 ◽  
Vol 7 (3.12) ◽  
pp. 1281
Author(s):  
Srinivasarao Gajula ◽  
Rajesh V
Keyword(s):  
Medical Image ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Image Watermarking ◽  
Scaling Factor ◽  
Security Requirements ◽  
Watermarking Scheme ◽  
Evaluation Parameters ◽  
Clear Information

In order to get clear information regarding patient it is necessary to enhance medical images like MRI, CT scan, ultrasound etc.  For clinical diagnosis, we have to transmit it through the communication network. During this process information must be protected from malicious users. In this process these images are manipulated, so to protect these images we have to follow some security requirements. In this paper, we are increasing the quality of the image by using enhancement with clahe technique and that enhanced image is watermarked for security purpose by using DWT, SVD transforms with a scaling factor as uniform distribution function. The performance evaluation parameters will give better results for medical as well as under water images. The obtained results are very helpful for integrity of medical images. The technique will provide better response for medical images. This method will give good results in terms of improvement in output, Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE).  

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