A the steganography factors that has to be

A Fuzzy Logic based Bit Plane Complexity
Segmentation steganography to secure Electronic Health Records

G. Santhi, B. Adithya

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Technology, Pondicherry Engineering

[email protected], [email protected]


Abstract— Electronic Healthcare or E-Healthcare (EH) is a
paperless management promises to speed up typical bureaucracy of healthcare. A
typical E-Healthcare system consists of many component and subsystem, such as
appointment, routine clinical notes, picture archiving, lab and radiology
orders, etc., are vulnerable to security threats. Cryptology and steganography
are generally used to ensure medical data security. For this reason, a Fuzzy
Logic based Bit Plane Complexity Segmentation (FL-BPCS) steganography is combined
with AES cryptography and Huffman lossless compression is proposed to secure
patient data’s. In this, Electroencephalogram time series, doctor’s comment and
patient information are selected as hidden data and Magnetic Resonance image
are used as cover image. 


steganography, FL-BPCS, Electroencephalogram, cryptology



The privacy and
security of EH information falls into two categories. First, inappropriare
releases from authorized users who intentionally or unintentionally access or
disseminare information violation of EH system policies or EH computer system
can be break by outsiders. Second, open disclosure of patient health
information to parties against to the interests of specific individual patient
or invadere a patient’s
These falls arises from the flow of data across the EH system amongst and
between providers, payers and secondary users, with or without the patient’s
knowledge are conceptually quite differ or require different counteracts and

obstaculum of the intrudere includes the use of firewalls to isolate internal
networks with strong encryption based authentication and authorization. There
is no known obstaculum for external networks like Denial-of-Service. Nevertheless,
technical countermeasures cannot be cures all security threats. Obstaculum such
as encryption, steganography and authentication are the only effective ways to
counter EH security threats against internet interface.      




Steganography is
used for covert communication 2. The embedding algorithm will convert the
cover medium into stego medium by embedding secret data into it. The inverse
process of embedding is done to extract the secret data. Imperceptibility,
security, capacity, robustness, embedding complexity are the steganography
factors that has to be considered. Image steganography is developed according
to its use in medical fields to communicate between patient as well as
communication between doctor’s and laboratory people’s to hide secret messages.
Steganography is to avoid drawing attention to the transmission of hidden
medical information. If suspicion is raised, steganography and cryptography is
planned to achieve the security of secret medical data’s. Both are complementary
to each other and provide better security, confidentiality and authenticity.

Eiji Kawaguchi
et al. proposed a Bit Plane Complexity Segmentation (BPCS) to increase the
embedding capacity and also to overcome the short comes of traditional
steganography techniques such as Least Significant Bit (LSB), Transform
embedding, Perceptual masking techniques. But the issue is embedded secret data
can be retrieved by using Difference Image Histogram (DIH) 8.

Karakis et al.
proposed a Fuzzy Logic based Least Significant Bit (FL-LSB) to reduce the
insecurity of LSB planes. By using fuzzy the LSB planes were chosen to embed
the secret data’s. But the issue, is low embedding capacity and the stego image
is invalid 9. This technique can be offered by using the following sections.
In section 2, the brief description of BPCS, cryptology as well as lossless compression
technique is presented, in section 3, the work methodology is presented, in
section 4, results are presented, in section 5, the discussion and analysis are
done, and finally the work is concluded.







Bit Plane Complexity Segmentation for embed and extract

steganography method makes use of the human vision. The cover image is divided
into informative region and noise-like region and the secret data is hidden in
noise blocks of vessel image without degrading image quality. The data is
hidden in both Most Significant Bit (MSB) as well as LSB planes 8.


with security concern

             Cryptology uses
the Advanced Encryption Standard (AES) to encrypt the secret data to form
second security layer. It comprises of a series of linked operations, some of
which involve replacing the inputs by specific outputs (Substitution) and others
involve shuffling bits around (Permutations). It treats the 128 bits of a
plaintext block as 16 bytes as a matrix. It has built-in flexibility of key
length, which allows a degree of future-proofing against progress in the
ability to perform exhaustive-key searches 4.


compression technique to reduce the size

compression technique uses the Huffman to reduce the secret data size. This
method takes a symbol (bytes) and encodes them with variable length codes
according to the statistical probabilities. A frequently used symbol will be
encoded with couple of bits, while symbol that are rarely used will be encoded
with more bits 1.



The proposed system aims to use EEG
signals and MR images that are obtained from same patients and to embed more
data with EEG into MR images of same patient. For this reason, the MR images
and EEG of epilepsy patients are gathered from the Department of Neurology at
Bonn University. 12 females and 11 males were included (age: 18-65 years; mean
age: 35 ± 7.7 years). The embedded message was combined with the patient’s
information, doctor’s comments, and EEG time series from the EEG file header.
The patient’s information (patient name, patient ID, patient birth date,
patient gender, patient age, patient weight, patient address, study
description, series date, series time, and series description) were separately
selected from the meta-header of each of the DICOM files.

Least Significant Bit (LSB) and Most
Significant Bit (MSB) embedding is a simple and fast strategy in steganography.
It has high imperceptibility and embedding capability. Hence, this study
proposes new methods to                                                                    

LSB and MSB embedding using medical data. The analyses consist of two stages:
embedding and extracting, respectively. Initially, the patient’s information is
obtained from DICOM series of epilepsy patient. The EEG data is segmented
according to the size of these DICOM images.

An image is sampled by pixels. In the
gray-scale image, pixels have gray level intensities. In color images, pixels
are also represented by three component intensities, being red (R), green (G),
and blue(B). A similarity measure is the similarity degree between two groups or
between two objects. In image processing, the similarity measure of two pixels
is used with distance information in Euclidean color space. Demirci 6
proposed a similarity-based method for edge detection. Furthermore, Pixel-Value
Differencing (PVD) or Adjacent Pixel Difference (APD) methods determine
embedding pixels in histogram-based steganography. Data Flow of overall
proposed system process is shown in Fig. 1. These methods have high embedding
capacity and PSNR values.

The main idea of this method is to
generate a new image whose pixels have double values at the interval 0 1.
This similar image of cover image is used to determine pixels for the embedding
message. In this method, if the values of similar pixels are higher than the
determined threshold by trial and error, they are selected to hide the message
9. The neighboring pixels of the image (P1,P2,…P9) using the 3×3 window
have three color component (R, G, B).

The gray level
differences of color components are calculated the neighboring pixels of the
stego image. The color distance of pixels are calculated by the Euclidean norm
9. Thesimilarity values of pixels are founded. Similarly, the coordinates of
the pixels are determined between the similarity values of pixels and threshold
values. The hidden message is extracted using the coordinates of the stego
image’s pixels.

Fig. 1 Data flow of the
overall proposed work


In message pre-processing stage,
lossless compression techniques, which are LZW (Lempel–Ziv–Welch) and Huffman
Compression, are used to increase message capacity. Furthermore, LZW and
Huffman Compression methods also ensure the complexity of the

To increase security, the compression message is encrypted by the Rijndael
symmetric encryption algorithm using a 128-bit key. Secondly, the proposed
methods, which are Fuzzy-Logic-based Bit Plane Complexity Segmentation (FL-BPCS),
select MSBs and LSBs of image pixels with using the differences in gray levels
of the pixels. Finally, the selected MSBs and LSBs of the pixels are altered
with the message bits in stego images. These processes are simultaneously run
with all DICOM series to decrease computational time. The extracting message
stage requires stego-DICOM images is shown in Fig. 2a, 2b and a stego-key,
which is the authentication key for decryption. Firstly, the proposed methods
give the pixels coordinates, which have an embedded message. These pixels are
used to gather the message. Secondly, the obtained message is decrypted and
decompressed. Finally, the patient’s information, segmented EEG, and the
doctor’s comments are displayed in a GUI (Graphical User Interface) screen. All
hidden EEG data can be also gathered from the DICOM series. The comparison
results of the proposed algorithm are evaluated by Histogram is shown in Fig.
3a, 3b, PSNR (peak signal-to-noise ratio), MSE (mean square of error), SSIM
(structural similarity measure), between the cover, and the stego-DICOM series.



The proposed system parameters
are used to evaluate the performance of the data hiding techniques.

Peak Signal to Noise Ratio (PSNR): The PSNR is generally used
to measure the quality of stego image in decibels (dB). Eq.1, gives the expression for
PSNR in which ICmax is
the maximum pixel value of the cover image and MSE is the mean square error:


In Eq. 2, x and y are the image coordinates, M and N are
the dimensions of the image, ISxy is
the generated stego-image and ICxy is
the cover image 7,10.

Structural similarity (SSIM) index: The SSIM is a method for finding
the similarity between cover image and the stego image. It is a
perception-based model that considers image degradation as perceived change in
structural information. The SSIM measure between two images  and  is represented in Eq. 3, where, is the average

,  is the average of is the variance of  is the variance of  is the covariance between  and   and k1, k2 are
two the variables used to stabilize the division with weak denominator 10.



(a)                                             (b)

Fig. 2: (a)Cover image
(b)Stego image



(a)                                             (b)

Fig. 3: Histogram of (a)Cover
image (b)Stego image

graph is created based on the embedding
capacity is shown in Fig. 4. For embedding payload, we use an embedding
rate, to represent the percentage of the embedded secret bits in the whole
pixels of the cover image 7. In Fuzzy Logic based Least Significant Bit the
embedding capacity is mentioned as low, because to embed the secret data it
occupies only the LSB positions. In Bit Plane Complexity Segmentation the
embedding capacity is mentioned as low, when compared to the Fuzzy Logic based
Least Significant Bit but normally the embedding capacity is high when compared
to the primitive LSB because to embed the secret data it occupies both MSB as
well as LSB. In Fuzzy Logic based Bit Plane Complexity Segmentation the
embedding capacity is high because to embed the secret data the red, green,
blue channel were used with fuzzy.

Fig. 4:
Embedding capacity


graph is created based on the performance evaluation parameters PSNR, MSE, SSIM is shown in Fig. 5. Peak signal-to-noise ratio,
often abbreviated PSNR, is
an engineering term for the ratio between the maximum possible power of a signal and the power of
corrupting noise that
affects the fidelity of its representation. PSNR
is most easily defined via the mean
squared error (MSE). Two of the error metrics used to compare the various image compression techniques are
the Mean Square Error (MSE) and the Peak Signal to Noise
Ratio (PSNR). The MSE is the cumulative squared error between the
compressed and the original image,
whereas PSNR is a
measure of the peak error 3. Structural Similarity Index (SSIM) is a perceptual metric
that quantifies image quality
degradation caused by processing such
as data compression or by losses in data transmission. It is a full reference
metric that requires two images from the same image capture— a reference image and a processed image

Fuzzy Logic based Least Significant Bit the PSNR is achieved high, because to
embed the secret data it occupies only the LSB positions and MSE is achieved
low when compared to Bit Plane Complexity Segmentation and SSIM is achieved
high. In Bit Plane Complexity Segmentation the PSNR is achieved as low because
to embed the secret data it occupies both MSB as well as LSB and MSE is
achieved high and SSIM is low because MSB is more sensitive. While changing
that pixel values with another pixel values must be matched. In Fuzzy Logic
based Bit Plane Complexity Segmentation the PSNR is low because to embed the
secret data MSB as well as LSB is used. MSE is low and SSIM is high because it
uses red, green, blue channels with fuzzy rules.


Fig. 5:
Performance Parameters

graph is created based on the steganalysis
is shown in Fig. 6. Visual
attacks involve observing the unusual patterns and noisy blurred regions in
some places of the stego image. A
statistical method called RS steganalysis for detection of LSB embedding uses
dual statistics derived from spatial correlation of an image. Histogram based steganalysis
techniques detect the existence of secret data from smoothness of the stego
image histogram. Similarly, a targeted active steganalysis technique is
implemented for HS embedding using the change in the characteristics of
histogram during data embedding 10.  In Fuzzy Logic based Least Significant Bit the
visual attacks, RS statistical attack, Sample Pair Analysis is achieved  high, because to embed the secret data it
occupies only the LSB positions but Difference Image Histogram is achieved low.
In Bit Plane Complexity Segmentation the visual attacks, RS statistical attack,
Sample Pair Analysis is achieved low, because to embed the secret data it
occupies both MSB as well as LSB but Difference Image Histogram is achieved
high. In Fuzzy Logic based Bit Plane Complexity Segmentation the visual
attacks, RS statistical attack, Sample Pair Analysis is achieved low, because
to embed the secret data the red, green, blue channel were used with fuzzy
rules but Difference Image Histogram is achieved high.








Fig. 6:
Detection of Embedded Data’s


In medical information system, medical
data is easily captured when being storing, receiving or transmission through
computer network and Internet. Cryptology and steganography are generally used
to ensure medical data security. For this reason, this study proposes new
algorithm, Fuzzy Logic-based Bit Plane Complexity Segmentation (FL-BPCS) to
secure medical data. EEG signals and MR images of epilepsy patients are used to
combine multiple medical signals into one file format. The embedding messages
are composed of EEG signals, doctor’s comment, and patient information in file
header of DICOM images. The messages are secured by using Huffman lossless
compression methods and Rijndael symmetric algorithm with 128 bit-key to
prevent the attacks.

The capacity of proposed algorithm is
higher than the result of similar studies in literature. According to the
obtained result, the proposed method ensures the confidentiality of the
patient’s information. The FL-BPCS method hides EEG signals, patient’s
information and doctor’s comment in the pixels of MR images. It also reduces
data repository and transmission capacity of the patients’ multiple medical
data. In future, the embedding and extracting of medical data from an cloud
storage with multiple records can be implemented for flexibility to access the
patient data for diagnosis. To access the patient data the grant privilege is
given to make read and write permission for authorized patient’s as well as doctor’s,
nurse with laboratory people’s.






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