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Telemedicine emerged as one of the much identified research
domain in academia-industries. Telemedicine Provides flawless processes,
seamless and maintains optimal medical data security. Steganography is also
most effective process for medical data security for image transformation
schemes essential data embedding. But Steganography
Technique’s potency depend on data embedding 8-12, pixel adjustment for maximum imperceptibility and efficiency percentage of image transformation. The techniques employed in Steganography is integer wavelet transform techniques (IWT) where used to in transform domain to develop real
time data embedding. IWT revert backs the integer form output hence it is less memory
consumer. Rahimi and Rabbani 13 scientists experimented IWT based
steganography for medical image security. They introduced blink water making
techniques technique which embeds the watermark bits in the singular value
vectors within the low pass sub bands within the contour let transform domain of DICOM images. This technique automatically
identifies a rectangular ROI and hides the watermark .In the experiment the
scrambled medical diagnostic image will be embedded to dummy cover image to that IWT are going to be applied then stego-image can be created fusing cover image with scrambled medical
diagnostic image. The drawback of this method is less embedding rate. Tiran
16 produced a method called value
difference expansion enable a high capacity reversible data embedding for image steganography.
DE 14 15 is to perform secret data hiding to difference the horizontal and
vertical image while HAAR wavelet transformation occurs  Lou et al. 17 introduced a lossless multiple-layer spatial data hiding scheme formedical image based on pixel-value differencing
expansion.  This method provides a high embedding rate and good quality
stego images by using reduced difference expansion technique to conceal the bit
stream in the LSBs of the expanded differences. J. Liu, G. Tang, and Y. Sun18, targeted on medical data confidentiality issue through
steganography. In this method cover images was at
initially transformed into one-dimensional
sequence by means of Hilbert filling curve, which was then processed for splitting into non-overlapping cluster of three pixels in each. Adaptive pixel pair match (APPM)
data embedding is used here as a result causes low distortion and hence high
imperceptibility. Later 19 derived a digital steganography model to hide
Electronic Patient Records (EPR) into medical diagnosis images. Exploited edge
detection 20 technique to recognize and embed secret data in spiky image-parts by applying Hamming code to embed three distinct secret message bits into 4
bits of the cover image. In 21 RT technique (Ripplet Transform Type-I) was exploited significantly to enablemultimodality Medical Image Fusion (MIF). Authors derived Ripplet Transform Type-I (RT) in conjunction with
Pulse-Coupled Neural Network (PCNN). Authors found that Ripplet Transform can
be a better alternative to perform image decomposition 2223 that eventually
could play vital role in medical image steganography. Above mentioned a number
of researches have been done to perform data embedding in images/medical image
using steganography techniques. All the approaches are focused on either PSNR
enhancement or embedding capacity enhancement using wavelet transform
technique. The key requirement like ROI preservation, maximum imperceptibility,
minimal or negligible histogram variations, statistical attack resilience,
higher PSNR has not been considered much. In Quality optimized medical imageinformation hiding algorithm that employs edge detection and data coding RSA based security key Encryption, Ripplet Transform, LSB embedding has been developed. The proposed method over comes the limitations of existing medical image
information hiding methods like high computational cost, limited embedding rate
by proposing a new data hiding technique. This technique achieves good balance between embedding
capacity and quality of the stego image.

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