A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD
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A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD Balasamy K 1
& Suganyadevi S
2
Received: 20 November 2019 / Revised: 18 September 2020 / Accepted: 24 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Nowadays secure medical image watermarking had become a stringent task in telemedicine. This paper presents a novel medical image watermarking method by fuzzy based Region of Interest (ROI) selection and wavelet transformation approach to embed encrypted watermark. First, the source image will undergo fuzzification to determine the critical points through central and final intensity along the radial line for selecting region of interest (ROI). Second, watermark image is altered to time-frequency domain through wavelet decomposition where the sub-bands are swapped based on the magnitude value obtained through logistic mapping. In the each sub-band all the pixels get swapped, results in fully encrypted image which guarantees the watermark to a secure, reliable and an unbreakable form. In order to provide more robustness to watermark image, singular values are obtained for encrypted watermark image and key component is calculated for avoiding false positive error. Singular values of the source and watermark image are modified through key component. Experimental results reveal that the proposed algorithm attains high robustness and improved security to the watermarked image against various kinds of attacks. Keywords Fuzzy ROI . Wavelet transform . Encryption . Key component . Watermarking
1 Introduction Telemedicine based medical image diagnosis is done through various techniques like computed tomography, ultrasound scanning, X-ray, magnetic resonance imaging, positron * Balasamy K [email protected]
1
Department of Information Technology, Dr.Mahalingam College of Engineering and Technology, Pollachi, India
2
Department of ECE, School of Engineering, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
Multimedia Tools and Applications
emission tomography. The investigative images are widely kept and it will undergo transmission for texture collection [8], image de-noising [2], segmentation [38], information hiding [34], and compression [12]. Consequently, medical images are scattered via hospital intranet and in the internet, where patient’s privacy information are available. However, hospital intranet will lack in the security issues related to patient information results in data leak [11, 28, 30]. With reference to visibility, medical image watermarking is categorized into visible and invisible domain [3, 19]. Further invisible watermarking is categorized into transform and time domain approaches [19, 20]. In transform domain approach, the host image will undergo transformation before embedding the watermark. Alternatively, time domain mechanism uses changes in the pixel values with respect to watermark information. Transform domain are classified into ridgelet transform [45], IW
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