A spatio-frequency orientational energy based medical image fusion using non-sub sampled contourlet transform

Devanna, H.; Kumar, G. A. E. Satish; Prasad, M. N. Giri

doi:10.1007/s10586-017-1351-0

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…NSCT is proved to be an effective transformation technique to avoid the pseudo-Gibbs phenomenon, and it can capture the higher dimensional singularities, such as edges and contours, that cannot be effectively represented by the wavelets and contourlet. 14 CLAHE can improve the overall contrast of underwater images and suppress noise amplification compared to other algorithms. 15 The contributions of this paper can be summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

“…To further improve the imaging quality of underwater images, we propose an underwater polarization image fusion method based on NSCT and CLAHE. NSCT is proved to be an effective transformation technique to avoid the pseudo-Gibbs phenomenon, and it can capture the higher dimensional singularities, such as edges and contours, that cannot be effectively represented by the wavelets and contourlet 14 . CLAHE can improve the overall contrast of underwater images and suppress noise amplification compared to other algorithms 15 …”

Section: Introductionmentioning

confidence: 99%

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Underwater polarization image fusion based on NSCT

Liu

Wang

et al. 2023

J. Electron. Imag.

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Affected by organic matter and suspended particles in the underwater environment, underwater images suffer from information loss and low contrast, and the underwater images obtained by traditional visible light imaging techniques are not effective. Based on this problem, a non-subsampled contourlet transform (NSCT)-based underwater polarization image fusion method is proposed in this paper. First, we use a division of time polarimeter and polarization imaging technique to acquire underwater target images. Then, the contrast limited adaptive histogram equalization algorithm and the two-dimensional median filtering algorithm preprocess the visible image and the degree of polarization image, respectively. After that, the lowfrequency and high-frequency sub-bands of the images are decomposed by the non-subsampled contourlet transform. The fusion rule for the low-frequency subband adopts the adaptive fuzzy method. For the high-frequency sub-bands, the fusion rule of taking the larger absolute value in pixels is used. In the experiment, popular methods were used to compare the advantages of the proposed method. The experimental results show that the proposed method is effective in improving the evaluation indexes, such as information entropy, enhancement measure evaluation, contrast, average gradient, and standard deviation. And the visual effect is much more comfortable to observe. The method is an effective fusion algorithm that can be applied to multiple material targets to improve the quality of underwater polarization imaging. The code is available at a Github repository at: https:// github.com/Si-Yu12/Underwater-polarization-image-fusion-based-on-NSCT.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Underwater polarization image fusion based on NSCT

Liu

Wang

et al. 2023

J. Electron. Imag.

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“…According to the National Laboratories WHO scientific guidelines, real-time polymerase chain reaction (RT-PCR) testing is the gold standard for validating the diagnosis of COVID-19. However, the RT-PCR test became inadequate, and showed high levels of false-negative results to confirm cases that were positive for COVID-19 [ 1 – 5 ]. In addition RT-PCR is a very time-consuming, complex manual process, and kits are currently lacking.…”

Section: Introductionmentioning

confidence: 99%

Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map

Bhattacharjee

Gupta

Chatterjee

2022

Appl Biochem Biotechnol

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In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.

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“…The watermark is integrated into the coefficients obtained by transforms. The wavelet transform of a signal allows representing the signal on a two-dimensional space called the time-scale plane, providing joint information on the signal in time and frequency [ 10 ]. The paving of the time–frequency plane induced by this transforms has the particularity of allowing a fine temporal resolution at high frequencies and a fine frequency resolution at low frequencies [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A robust blind medical image watermarking approach for telemedicine applications

et al. 2021

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In order to enhance the security of exchanged medical images in telemedicine, we propose in this paper a blind and robust approach for medical image protection. This approach consists in embedding patient information and image acquisition data in the image. This imperceptible integration must generate the least possible distortion. The watermarked image must present the same clinical reading as the original image. The proposed approach is applied in the frequency domain. For this purpose, four transforms were used: discrete wavelets transform, non-subsampled contourlet transform, non-subsampled shearlet transform and discreet cosine transform. All these transforms was combined with Schur decomposition and the watermark bits were integrated in the upper triangular matrix. To obtain a satisfactory compromise between robustness and imperceptibility, the integration was performed in the medium frequencies of the image. Imperceptibility and robustness experimental results shows that the proposed methods maintain a high quality of watermarked images and are remarkably robust against several conventional attacks. Keywords Medical image Á Digital watermarking Á Discrete wavelet transform Á Non-subsampled contourlet transform Á Non-subsampled shearlet transform Á Discreet cosine transform Á Schur decomposition

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A spatio-frequency orientational energy based medical image fusion using non-sub sampled contourlet transform

Cited by 7 publications

References 25 publications

Underwater polarization image fusion based on NSCT

Underwater polarization image fusion based on NSCT

Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map

A robust blind medical image watermarking approach for telemedicine applications

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