Medical Image Transmission Using Novel Crypto-Compression Scheme

Mashat, Arwa; Bhatia, Surbhi; Kumar, Ankit; Dadheech, Pankaj; Alabdali, Aliaa M.

doi:10.32604/iasc.2022.021636

Cited by 7 publications

(4 citation statements)

References 38 publications

(38 reference statements)

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“…Mashat et al proposed a novel technique for medical image transmission where the paper focuses on security aspects of medical information during transmission to authenticate and protect the information over public networks. The technique is robust and assures the prevention of stealing of medical data over transmission network [29]. Bhatia [30] has given a comparative analysis of opinion summarization techniques paper covers both abstractive and extractive approaches where extractive approaches use Principal component analysis for summarization of the text resulting in a reduction of dimensions in data aspects without any loss of information irrespective of domain.…”

Section: Related Work Background Preliminary Studies and Literature R...mentioning

confidence: 99%

Image Enhancement Using Adaptive Fractional Order Filter

Heena¹,

Biradar²,

Maroof³

et al. 2023

Computer Systems Science and Engineering

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Image enhancement is an important preprocessing task as the contrast is low in most of the medical images, Therefore, enhancement becomes the mandatory process before actual image processing should start. This research article proposes an enhancement of the model-based differential operator for the images in general and Echocardiographic images, the proposed operators are based on Grunwald-Letnikov (G-L), Riemann-Liouville (R-L) and Caputo (Li & Xie), which are the definitions of fractional order calculus. In this fractional-order, differentiation is well focused on the enhancement of echocardiographic images. This provoked for developing a non-linear filter mask for image enhancement. The designed filter is simple and effective in terms of improving the contrast of the input low contrast images and preserving the textural features, particularly in smooth areas. The novelty of the proposed method involves a procedure of partitioning the image into homogenous regions, details, and edges. Thereafter, a fractional differential mask is appropriately chosen adaptively for enhancing the partitioned pixels present in the image. It is also incorporated into the Hessian matrix with is a second-order derivative for every pixel and the parameters such as average gradient and entropy are used for qualitative analysis. The wide range of existing state-of-the-art techniques such as fixed order fractional differential filter for enhancement, histogram equalization, integer-order differential methods have been used. The proposed algorithm resulted in the enhancement of the input images with an increased value of average gradient as well as entropy in comparison to the previous methods. The values obtained are very close (almost equal to 99.9%) to the original values of the average gradient and entropy of the images. The results of the simulation validate the effectiveness of the proposed algorithm.

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Section: Related Work Background Preliminary Studies and Literature R...mentioning

confidence: 99%

Image Enhancement Using Adaptive Fractional Order Filter

Heena¹,

Biradar²,

Maroof³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…Yet another survey of application of cryptography mechanism using neural networks was investigated in [11]. A secure cryptography-based medical image reclamation method integrating discrete cosine transform, steganography, and watermarking was presented in [12]. By means of integration the method was not only found to be robust but was also found to be hard stolen from malicious users.…”

Section: Related Workmentioning

confidence: 99%

Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

Ansari,

Tamilselvan

2023

IJRITCC

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Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance. The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost.

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“…In disease detection using medical data [ 47 – 49 ], researchers not need to concern regarding where the main features are in the image. Furthermore, the system is capable of capturing spatial-spectral correlations in MRI neuroscans [ 50 ], particularly if there is an occurrence of three-dimensional and 2-dimensional referring to medical image, via a process of minimization and potential optimization settings [ 51 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…In disease detection using medical data [47][48][49], researchers not need to concern regarding where the main features are in the image. Furthermore, the system is capable of capturing spatial-spectral correlations in MRI neuroscans [50],…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices

Sethi

Rani

Singh

et al. 2022

Computational and Mathematical Methods in Medicine

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Developments in medical care have inspired wide interest in the current decade, especially to their services to individuals living prolonged and healthier lives. Alzheimer’s disease (AD) is the most chronic neurodegeneration and dementia-causing disorder. Economic expense of treating AD patients is expected to grow. The requirement of developing a computer-aided technique for early AD categorization becomes even more essential. Deep learning (DL) models offer numerous benefits against machine learning tools. Several latest experiments that exploited brain magnetic resonance imaging (MRI) scans and convolutional neural networks (CNN) for AD classification showed promising conclusions. CNN’s receptive field aids in the extraction of main recognizable features from these MRI scans. In order to increase classification accuracy, a new adaptive model based on CNN and support vector machines (SVM) is presented in the research, combining both the CNN’s capabilities in feature extraction and SVM in classification. The objective of this research is to build a hybrid CNN-SVM model for classifying AD using the MRI ADNI dataset. Experimental results reveal that the hybrid CNN-SVM model outperforms the CNN model alone, with relative improvements of 3.4%, 1.09%, 0.85%, and 2.82% on the testing dataset for AD vs. cognitive normal (CN), CN vs. mild cognitive impairment (MCI), AD vs. MCI, and CN vs. MCI vs. AD, respectively. Finally, the proposed approach has been further experimented on OASIS dataset leading to accuracy of 86.2%.

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Medical Image Transmission Using Novel Crypto-Compression Scheme

Cited by 7 publications

References 38 publications

Image Enhancement Using Adaptive Fractional Order Filter

Image Enhancement Using Adaptive Fractional Order Filter

Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission

A CAD System for Alzheimer’s Disease Classification Using Neuroimaging MRI 2D Slices

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