A self recovery approach using halftone images for medical imagery

A Self Recovery Approach Using Halftone
Images for Medical Imagery System
John Blesswin, Rema and Jenifer Joselin, Karunya University, India

 easier that the hackers can grab or duplicate the medical
Abstract— Security has become an inseparable issue even in information on the Internet. While using secret images,
the field of medical applications. Communication in medicine security issues should be taken into consideration because
and healthcare is very important. The fast growth of the
hackers may utilize this weak link over communication
exchange traffic in medical imagery on the Internet justifies the
creation of adapted tools guaranteeing the quality and the network to steal the information that they want. To deal with
confidentiality of the information while respecting the legal and the security problems of secret images, various image secret
ethical constraints, specific to this field. Visual Cryptography is sharing schemes have been developed.
the study of mathematical techniques related aspects of Visual cryptography scheme [4] eliminates complex
Information Security which allows Visual information to be computation problem in decryption process thus enabling the
encrypted in such a way that their decryption can be performed
transfer of medical images in a more convenient, easy and
by the human visual system, without any complex cryptographic
algorithms. This technique represents the secret image by secure way. Even with the remarkable advance of computer
several different shares of binary images. It is hard to perceive technology, using a computer to decrypt secrets is infeasible
any clues about a secret image from individual shares. The in some situations. For example, a security guard checks the
secret message is revealed when parts or all of these shares are badge of an employee or a secret agent recovers an urgent
aligned and stacked together. In this paper we provide an secret at some place where no electronic devices are applied.
overview of the emerging Visual Cryptography (VC) techniques
used in the secure transfer of the medical images over in the
In these situations the human visual system is one of the most
internet. The related work is based on the recovering of secret convenient and reliable tools to do checking and secret
image using a binary logo which is used to represent the recovery. Visual cryptography (VC), proposed by Naor and
ownership of the host image which generates shadows by visual Shamir [1], is a method for protecting image-based secrets
cryptography algorithms. An error correction-coding scheme is that has a computation-free decryption process.
also used to create the appropriate shadow. The logo extracted
from the half-toned host image identifies the cheating types.
Furthermore, the logo recovers the reconstructed image when
shadow is being cheated using an image self-verification scheme
based on the Rehash technique which rehash the halftone logo
for effective self verification of the reconstructed secret image
without the need for the trusted third party(TTP).

Index Terms—Visual secret sharing, Medical image,
Halftoning, Verifying shares, Cryptography

I. INTRODUCTION
Fig. 1. Construction of (2, 2) VC Scheme

T HE rapid advancement of network technology,
multimedia information is transmitted over the Internet If ‘p’ is white, one of the two columns under the white
conveniently. Nowadays, the transmission of medical pixel in Figure 1 is selected. If p is black, one of the two
information has become very convenient due to the generality columns under the black pixel is selected. In each case, the
of Internet. The current needs in medical imaging security selection is performed randomly such that each column has
come mainly from the development of the traffic on Internet 50% probability to be chosen. Then, the first two pairs of sub
(tele-expertise, tele-medicine) and to establishment of medical pixels in the selected column are assigned to share 1 and
personal file. Various confidential data such as the secure share 2, respectively. Since, in each share, p is encrypted into
transfer medical images are transmitted over the Internet. a black–white or white–black pair of sub pixels, an individual
Internet has created the biggest benefit to achieve the share gives no clue about the secret image [2].
transmission of patient information efficiently. However, it is

By stacking the two shares as shown in the last row of corresponding HI(x,y) is set as 255[16] and its neighbouring
Figure 1, if ‘p’ is white it always outputs one black and one pixels values must be decreased. In contrast, the value of
white sub pixel, irrespective of which column of the sub pixel GI(x,y) is quantized to zero, and its neighbouring pixels
pairs is chosen during encryption. If ‘p’ is black, it outputs values must be increased.
two black sub pixels. Hence there is a contrast loss in the
reconstructed image. However the decrypted image is visible
to naked eye since human visual system averages their
individual black–white combinations. The important
parameters of this scheme are,
a) Pixel expansion ‘m’, which refers to the number of
pixels in a share used to encrypt a pixel of the secret image.
This implies loss of resolution in the reconstructed image. Fig. 2. Flowchart of Error Diffusion architecture
b) Contrast ‘α’, which is the relative difference between
black and white pixels in the reconstructed image. This
implies the quality of the reconstructed image. Generally,
smaller the value of m will reduce the loss in resolution and
greater the value of ‘α’ will increase the quality [3] of the
reconstructed image. As mentioned above if ‘m’ is decreased, Fig. 3. Kernel weight of Floyd and Steinberg’s Error Filter
the quality of the reconstructed image will be increased but
security will be a problem. So research is focused on two First, Set (x,y) as (1, 1); that is, the first pixel is taken into
paths, consideration. Then Compute error value E(x,y) = GI(x,y) -
HI(x,y) and corresponding pixel value HI(x,y) in the halftone
1. To have good quality reconstructed image. image [8] for pixel located at coordinates (x,y) in grayscale
2. To increase security with minimum pixel expansion. image GI. Diffuse error E(x,y) over four neighbouring
pixels. The four neighbouring pixels altered in this equation
II. GENERATION OF HALFTONE IMAGES are GI (x,y+1) , GI(x+1,y- 1), GI(x+ 1,y), and GI(x+ 1,y+ 1).
Their modified values are computed based on the kernel
A. Error Diffusion Technique weight of the error filter as shown in Figure 3 demonstrates
Error diffusion is a type of halftoning in which the the kernel weight of Floyd and Steinberg’s error filter.
quantization residual is distributed to neighbouring pixels
that have not yet been processed. The simplest form of the III. PROPOSED SCHEME
algorithm scans the image one row at a time and one pixel at This section presents a detailed description of a novel VSS
a time. The current pixel is compared to a half-gray value [6]. scheme, called a self recovery approach, proposed for
If it is above the value a white pixel is generated in the grayscale images [6] that can be applied to the images used in
resulting image. If the pixel is below the half way brightness, the medical applications. The images used in the medical
a black pixel is generated. The generated pixel is either full would be color images [5]. In this case, first, a color image is
bright, or full black, so there is an error in the image. The decomposed into three sub-images: red, green and blue.
error is then added to the next pixel in the image and the Secondly, the scheme is applied independently to each sub-
process repeats as illustrated in Figure 2. The simple and image individually. Lastly, the reconstructed secret color is
attractive concept of this technique is the diffusion of errors to generated by concatenating the three reconstructed grayscale
neighbouring pixels; thus, image luminance is not lost. The components together [10].
diffused image is generated based on an error diffusion This technique can be used to convert these medical color
strategy also called an error filter. Each error filter has a set images [5] to gray scale and apply the VSS scheme. In our
of kernel weights. proposed scheme, a halftone image HI [8] is created from the
The kernel weights of Floyd and Steinberg’s error filter are grayscale secret image GI (medical image) by using an error
7/16, 5/16, 3/16, and 1/16, shown in Figure 3. After a diffusion technique.
quantization procedure, a pixel GI(x,y) at position (x,y) in A half-sampled image of the halftone image HI [8], called
grayscale image GI(x,y) [6] becomes HI(x,y) and has a value a halftone logo HL, is created by using an interpolation
of either 0 or 255.The threshold TH is used to determine technique [12]. In our scheme, the halftone logo HL is used to
HI(x,y) and the quantization error is determined as E(x,y) = ascertain the reliability of the reconstructed grayscale secret
GI(x,y) - HI(x,y). A signal consisting of past error values is image [10] GI and the judiciousness of the set of collected
passed through the error filter to produce a correction factor shadows as shown in Figure 4. Full details of generating a
that is added to future input pixels. If the quantization error is reconstructed gray scale image and self recovering the
negative, GI(x,y) is quantized as 255 so that the grayscale image is presented in four steps as follows.

Fig. 4. Flowchart of proposed scheme

A. Generation of Shares
Step1: Substitution
Shadows are created for medical-image in the Share
First, we replace the rightmost two bits of every pixel with
construction step. In this step Apply the error diffusion
0 to generate a transformed halftone image (HL') based on the
technique to the grayscale image GI to retrieve a halftone
simple LSE substitution scheme. From the images we can
image HI, the width and height of HI are W and H. The
found 2-bits substitution can make sure images with high
halftone logo [7], named HL, which is a half-sample of HI, is
quality compared with 4-bits substitution.
created by using the interpolation [12] and error diffusion
techniques. In this step, the halftone logo HL is shrunk to Step2: Generating Hit values
one-half of halftone image HI in each dimension. Randomly We then generate a secure key SK, and use the MHIT
generate two symmetric keys K1 and K2. Encrypt pixels of procedure of the first level rehash model to treat every pixel
HL with key K1 and symmetric cryptographic algorithm, value of HL' as the key in the key space. A fad is worth
such as DES, when pixels are located at even rows of halftone mentioning is that in rehash technique the assumed key
image HL, and then encrypt pixels of halftone image HL with values to be non-equal in defining the key space when they
key K2 and symmetric cryptographic algorithm when pixels built the perfect hash scheme. Pixel values can be identical in
are located at odd rows of halftone image HL to derive the a certain halftone logo image, in other words, the keys in the
encrypted halftone logo HL. Then generate the shares using key space could be the same. This scheme generates a self-
image clustering, interpolation techniques [13] and the key verification code for the halftone logo, not to locate a unique
will be embedded into the shares. corresponding position in the address space for them. We
B. Self-Verifying code Embedding Phase follow the definition of keyed hash function to randomly
select three keyed hash functions. The values are expressed in
A half-sampled image of the halftone image HI [8], called
binary form.
a halftone logo HL, created by using an interpolation
technique [13] is rehashed using the first level rehash Step 3: Embedding
technique of the Self-verifying code embedding. This The HIT value will then be orderly embedded back into the
technique generates a binary self-verification code for every rightmost two bits of every pixel to generate a halftone logo
pixel and inserts the code back into the rightmost two bits of image (HL) with self-verification capabilities[15]. After the
every pixel, and thus a halftone logo with self-verification above three steps, every pixel goes through substitution and
capabilities can be produced as shown in Figure 5. the modified HIT construction procedure, and then the
For the purpose of explanation, we assume that the size of corresponding HIT values can be derived. If, at a later date,
the halftone logo (HL) [7] is n x n, with 8-bit resolution. The the same HIT values are obtained after the pixels go through
following is the three stages to equip halftone logo with the the same processes, we can conclude that the pixels have not
self verification capabilities: been tampered. If different HIT values are derived, then the

Fig.5. Self-Verification Code Embedding

pixels have been tampered. Due to the number of keyed XOR operation on the intermediate shadows S1 and S2.
hash functions is related to the number of self-verification Because the intermediate shadows S1 and S2 are binary
codes which are not zero, in other words and then image images containing 7×X pixels, where X= [(W×H)/7], HI’ is a
receivers can identify the illegal tampering from attackers binary image consisting of 7×X pixels. Apply the inverse
more easily. To increase the effectiveness of self-verification halftoning technique ELIH to the halftone image HI’ to
codes, sender and receiver can generate more keyed hash generate the intermediate reconstructed image.
functions. However, it will increase the transmission load.
D. Verifying Phase
Based on Du et at's original concept of first level rehash
scheme, we suggest halftone image senders at least use three This phase verifies the reliability of the reconstructed secret
keyed hash functions to guarantee the effectiveness of the image and the set of collected shadows. The halftone image
self-verification codes. HI, which is generated from in the revealing phase, perform
As to the keys which are used in each keyed hash the half-sampling by applying error diffusion and
functions, they can be identical or different. Even all key are interpolation techniques [13] to retrieve another halftone
same, they still will not decrease the effectiveness of self image, called HI. In this phase the halftone logo HL
verification codes. In the previous self verifying scheme the generated from the halftoned image HI is rehashed using the
dealer must register his/her issued logo with the trusted third rehash technique [14] which generates a binary self-
party (TTP) before s/he distributes the shadows to participants verification code for every pixel and insert the code back into
during the shares construction phase. After receiving the the rightmost two bits of every pixel, thus an halftone logo
logo, the TTP checks whether the logo is the same as the half- with self-verification capabilities is formed.
sampling result of the halftone secret image. If they are the The HL is the extracted halftone image whose original
same, the TTP accepts the dealer’s request; otherwise, the image is the halftone logo HL` and HL is the half sampled
TTP rejects the dealer’s request. This paper introduces an image of HI. The reconstructed halftone logo HL depends on
image self-verification scheme based on modified Du et al’s the intermediate shadow S1, which is only extracted from
first level rehash [14] scheme which rehash the halftone logo shadow SH1. If there is no cheating, the intermediate shadow
for effective self verification of the reconstructed secret image S1 in the revealing phase is the same as the intermediate
without the need for the trusted third party(TTP). shadow S1 in the shares construction phase. In other words,
the halftone logo HL` is the same as halftone logo HL when
C. Revealing Phase no cheating occurs [11].
This section describes in detail how to extract the halftone
E. Image Recovering Phase
logo HL and the reconstructed secret grayscale GI [10] from
the set of collected shadows. By using the reversible data This phase recovers the reconstructed secret image when
hiding scheme [9] , the first key K1 and the intermediate the shadow is being cheated. The cheated image is recovered
shadow S1 are derived from the shadow SH. Similarly, the by applying Double-Sampling and Inverse halftoning[13]. In
second key K2 and the intermediate shadow S2 are derived this first, find the value of d which is the difference between
from the shadow SH2. Then Divide the first intermediate HL` and HI``, d = HL` - HI``. When the value of d is equal to
shadow S1 into non-overlapping 7-pixel blocks. Then, zero, then the reconstructed secret image GI (medical image)
multiply each 7-pixel block by a P (7, 4) Hamming code. is generated completely from HI` by inverse halftoning
Based on X blocks divided from 7×X pixels in the transformation.
intermediate shadow S1, we obtain a set of X blocks, with Obviously, when d is not equal to zero, if a fake shadow
each block consisting of 3 bits. By combining these X blocks, drops in the first shadow, the reconstructed image GI` is
reconstruct the encrypted halftone logo eHL’. usually a noise-like image and extracted halftone logo HL` is
Decrypt extracted encrypted halftone image HL by using either a noise-like image or a meaning halftone image. If the
keys K1 and K2 for pixels located in even rows and odd rows fake shadow is the second one, a noise-like image GI` is
in the encrypted halftone image HL, respectively. After the generated in addition to a meaning halftone image HL`[8]. In
decryption is completed, the extract halftone image HL’ is this case, we not only know that GI` is fake but also can
obtained. Create the halftone image HI by performing the recover GI` by using HL`.

Table 1. Reconstructed image quality and reliability conclusion when no cheating is detected

To recuperate GI from HL`, we first perform double- applications. Our scheme not only protects an original
sampling by applying an interpolating operation into HL` to medical secret image by dividing it into n shadows but also
retrieve HI`. verifies the reconstructed medical secret image and identifies
the cheating types using the self verifiable rehash technique
IV. EXPERIMENTAL RESULTS when some of collected shadows are forged during the
Experimental results on medical images demonstrate three revealing process. Moreover, the original reconstructed
objectives. Thus more than 100 medical images have been medical secret image is established only when k out of n valid
tested. Sample tested medical images are given in Table 1. shadows are collected and no one can force the honest
The first is the generation of the constructed secret image participant to reconstruct a wrong secret image. Error
with high quality, with no computational complexity and no diffusion, image clustering, and inverse halftoning are three
pixel expansion. The second is the reconstruction of images techniques employed as foundation of this scheme. Based on
and verification of the reliability of the set of collected the Boolean operator XOR, this mechanism can easily recover
shadows as well as the reconstructed secret image. In our the reconstructed medical image from the collected shadows
scheme, peak signal-to-noise ratio (PSNR) is used to evaluate without adding computational complexity in the revealing
the quality of the reconstructed original image GI`. Similarly, and verifying phase. Thus, it is best to use for images used in
we use mean square error (MSE) to identify the difference the medical applications for transferring images over the
between the extracted halftone logo HL` and halftone image Internet.
HI``. The reliability of the VSS scheme is guaranteed if MSE
is equal to zero. The third objective is the image self- ACKNOWLEDGMENT
verification code embedding phase for the reliability of HL The Authors would like to thank the Innovative Transtar
followed by the recovering of images. Experiments were Research Team in Karunya University, for supporting us in
based two assumptions corresponding to two circumstances. our research work.
The first circumstance assumes that neither the dealer nor the
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