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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 critical data embedding. But
Steganography Technique’s efficiency depends on data embedding 8-12, pixel
adjustment for maximum imperceptibility and efficiency percentage of image
transformation. The techniques used in Steganography is integer wavelet transform
techniques (IWT) where used to in transform domain to develop real time data
embedding module. 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 in
the contourlet transform domain of DICOM images. This method 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
which IWT will be applied then stego-image can be obtained 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 for medical 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.

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J. Liu, G. Tang, and Y.
Sun 18, focused on medical data confidentiality issue through steganography.
In this method cover images was at first transformed into one-dimensional
sequence by means of Hilbert filling curve, which was then processed for
splitting into non-overlapping clusters 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 enable multimodality 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 image information 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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