A Multiple Criteria-Based Cost Function Using Wavelet and Edge Transformation for Medical Image Steganography

Nipanikar, Sanjay I.; Deepthi, V. Hima

doi:10.1515/jisys-2016-0095

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Watermarking refers to an order of numeral bits positioned in a digital concealment object that recognize the document's patent information [25], [26]. Steganography convert message and also modifies the image in a way that merely the disseminator of the message and the projected receiver would be able to discover the message while being sent [27]- [31]. In RDH, concealment object holds the secret data as well [32]- [34].…”

Section: Introductionmentioning

confidence: 99%

Efficiency of LSB steganography on medical information

Abikoye

Ogundokun

2021

IJECE

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The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder.

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Section: Introductionmentioning

confidence: 99%

Efficiency of LSB steganography on medical information

Abikoye

Ogundokun

2021

IJECE

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“…The bat optimization algorithm is modified in (Kaur et al , 2017) for segmenting the brain tumor images. The optimization techniques (Dhumane and Prasad, 2017; Nipanikar et al , 2017; Shelke and Prasad, 2018; Bhopale et al , 2014) have applications in brain tumor segmentation. Also, literature has used the semiautomatic brain tumor segmentation (Sauwen et al , 2017) approaches.…”

Section: Introductionmentioning

confidence: 99%

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Raju¹,

Pabboju

Rao

2019

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Purpose Brain tumor segmentation and classification is the interesting area for differentiating the tumorous and the non-tumorous cells in the brain and classifies the tumorous cells for identifying its level. The methods developed so far lack the automatic classification, consuming considerable time for the classification. In this work, a novel brain tumor classification approach, namely, harmony cuckoo search-based deep belief network (HCS-DBN) has been proposed. Here, the images present in the database are segmented based on the newly developed hybrid active contour (HAC) segmentation model, which is the integration of the Bayesian fuzzy clustering (BFC) and the active contour model. The proposed HCS-DBN algorithm is trained with the features obtained from the segmented images. Finally, the classifier provides the information about the tumor class in each slice available in the database. Experimentation of the proposed HAC and the HCS-DBN algorithm is done using the MRI image available in the BRATS database, and results are observed. The simulation results prove that the proposed HAC and the HCS-DBN algorithm have an overall better performance with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Design/methodology/approach The proposed HAC segmentation approach integrates the properties of the AC model and BFC. Initially, the brain image with different modalities is subjected to segmentation with the BFC and AC models. Then, the Laplacian correction is applied to fuse the segmented outputs from each model. Finally, the proposed HAC segmentation provides the error-free segments of the brain tumor regions prevailing in the MRI image. The next step is to extract the useful features, based on scattering transform, wavelet transform and local Gabor binary pattern, from the segmented brain image. Finally, the extracted features from each segment are provided to the DBN for the training, and the HCS algorithm chooses the optimal weights for DBN training. Findings The experimentation of the proposed HAC with the HCS-DBN algorithm is analyzed with the standard BRATS database, and its performance is evaluated based on metrics such as accuracy, sensitivity and specificity. The simulation results of the proposed HAC with the HCS-DBN algorithm are compared against existing works such as k-NN, NN, multi-SVM and multi-SVNN. The results achieved by the proposed HAC with the HCS-DBN algorithm are eventually higher than the existing works with the values of 0.945, 0.9695 and 0.99348 for accuracy, sensitivity and specificity, respectively. Originality/value This work presents the brain tumor segmentation and the classification scheme by introducing the HAC-based segmentation model. The proposed HAC model combines the BFC and the active contour model through a fusion process, using the Laplacian correction probability for segmenting the slices in the database.

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Optimized support vector neural network and contourlet transform for image steganography

Reshma

Kumar

et al. 2020

Evol. Intel.

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A Multiple Criteria-Based Cost Function Using Wavelet and Edge Transformation for Medical Image Steganography

Cited by 5 publications

References 26 publications

Efficiency of LSB steganography on medical information

Efficiency of LSB steganography on medical information

Hybrid active contour model and deep belief network based approach for brain tumor segmentation and classification

Optimized support vector neural network and contourlet transform for image steganography

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