Forgery detection in medical images using Complex Valued Neural Network (CVNN)

With the advent of telemedicine and telediagnosis over the internet, medical images are watermarked to ensure it integrity and authenticity. The current problem with the watermarking system used for medical images is distortion introduced during the patient data/information embedding. This factor ha...

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Main Authors:	Olanrewaju, Rashidah Funke, Khalifa, Othman Omran, Hassan Abdalla Hashim, Aisha, Zeki, Akram M., Aburas, Abdurazzag Ali
Format:	Article
Language:	English
Published:	INSI Publications 2011
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/6983/ http://irep.iium.edu.my/6983/ http://irep.iium.edu.my/6983/1/Forgery_Detection_in_Medical_Images_Using_Complex_Valued_Neural_Network1251-1264.pdf

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recordtype	eprints
spelling	iium-69832017-06-13T07:19:46Z http://irep.iium.edu.my/6983/ Forgery detection in medical images using Complex Valued Neural Network (CVNN) Olanrewaju, Rashidah Funke Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha Zeki, Akram M. Aburas, Abdurazzag Ali T Technology (General) With the advent of telemedicine and telediagnosis over the internet, medical images are watermarked to ensure it integrity and authenticity. The current problem with the watermarking system used for medical images is distortion introduced during the patient data/information embedding. This factor has hindered proper detection and treatment. A new technique for detecting forgery in medical watermarked image using CVNN is proposed in this paper. Capabilities of Neural Networks features have been exploited using the Complex version of ANN, trained by Complex backpropagation (CBP) algorithm. This technique was used to embed and detect forge watermark in Fast Fourier Transform FFT domain. The performance of the algorithm has been evaluated using mammogram images. The imperceptibility and detection accuracy was appraised with objective performance measure; Detector response, PSNR, BER, IFM SSIM and Normalize Correlation. Results indicate that watermarked mammogram were perceptually indistinguishable from the host mammogram, hence the application of the developed CVNN-based watermarking technique in medical images can improve correct diagnoses. Ability of the algorithm to localize modification undergone makes it a unique and efficient algorithm for authentication and tamper detection as well as blind detection applications. INSI Publications 2011 Article PeerReviewed application/pdf en http://irep.iium.edu.my/6983/1/Forgery_Detection_in_Medical_Images_Using_Complex_Valued_Neural_Network1251-1264.pdf Olanrewaju, Rashidah Funke and Khalifa, Othman Omran and Hassan Abdalla Hashim, Aisha and Zeki, Akram M. and Aburas, Abdurazzag Ali (2011) Forgery detection in medical images using Complex Valued Neural Network (CVNN). Australian Journal of Basic and Applied Sciences, 5 (7). pp. 1251-1264. ISSN 1991-8178 http://www.insipub.com/ajbas/2011/July-2011/1251-1264.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Olanrewaju, Rashidah Funke Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha Zeki, Akram M. Aburas, Abdurazzag Ali Forgery detection in medical images using Complex Valued Neural Network (CVNN)
description	With the advent of telemedicine and telediagnosis over the internet, medical images are watermarked to ensure it integrity and authenticity. The current problem with the watermarking system used for medical images is distortion introduced during the patient data/information embedding. This factor has hindered proper detection and treatment. A new technique for detecting forgery in medical watermarked image using CVNN is proposed in this paper. Capabilities of Neural Networks features have been exploited using the Complex version of ANN, trained by Complex backpropagation (CBP) algorithm. This technique was used to embed and detect forge watermark in Fast Fourier Transform FFT domain. The performance of the algorithm has been evaluated using mammogram images. The imperceptibility and detection accuracy was appraised with objective performance measure; Detector response, PSNR, BER, IFM SSIM and Normalize Correlation. Results indicate that watermarked mammogram were perceptually indistinguishable from the host mammogram, hence the application of the developed CVNN-based watermarking technique in medical images can improve correct diagnoses. Ability of the algorithm to localize modification undergone makes it a unique and efficient algorithm for authentication and tamper detection as well as blind detection applications.
format	Article
author	Olanrewaju, Rashidah Funke Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha Zeki, Akram M. Aburas, Abdurazzag Ali
author_facet	Olanrewaju, Rashidah Funke Khalifa, Othman Omran Hassan Abdalla Hashim, Aisha Zeki, Akram M. Aburas, Abdurazzag Ali
author_sort	Olanrewaju, Rashidah Funke
title	Forgery detection in medical images using Complex Valued Neural Network (CVNN)
title_short	Forgery detection in medical images using Complex Valued Neural Network (CVNN)
title_full	Forgery detection in medical images using Complex Valued Neural Network (CVNN)
title_fullStr	Forgery detection in medical images using Complex Valued Neural Network (CVNN)
title_full_unstemmed	Forgery detection in medical images using Complex Valued Neural Network (CVNN)
title_sort	forgery detection in medical images using complex valued neural network (cvnn)
publisher	INSI Publications
publishDate	2011
url	http://irep.iium.edu.my/6983/ http://irep.iium.edu.my/6983/ http://irep.iium.edu.my/6983/1/Forgery_Detection_in_Medical_Images_Using_Complex_Valued_Neural_Network1251-1264.pdf
first_indexed	2023-09-18T20:16:11Z
last_indexed	2023-09-18T20:16:11Z
_version_	1777407803195916288

Forgery detection in medical images using Complex Valued Neural Network (CVNN)

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