50120130406010

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 6, November - December (2013), © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 6, November - December (2013), pp. 95-102
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
www.jifactor.com

IJCET
©IAEME

COMPREHENSIVE ASSESSMENT, PERFORMANCE OVERVIEW AND
REVIEW OF ALL EXISTING TECHNIQUES OF REVERSIBLE IMAGE
WATERMARKING FOR MEDICAL IMAGING
Ashvini Bhamare1, Prof. Sachin Sonawane2, Prof. ShashikantPatil3
1

MPSTME, SVKM’s NMIMS, Maharashtra, India
3
2

ABSTRACT
Watermarking describes methods to hide information, such as a number or text or image in
digital media format like image, video or audio. Watermarking techniques can be classified as
Visible, Invisible, Robust and Fragile.This paper focuses on reversible watermarking, which is one
of the methods toimplement fragile watermarking. Reversible watermarking is a data hiding
technique andit restores the original image without any distortion. Now a day’s, various techniques
are well known to perform reversible watermarking such as Histogram Shifting (HS), Difference
Expansion (DE), Prediction Error Expansion(PEE), Integer Transform, Least Significant Bit (LSB),
Wavelet and Interpolation Technique etc. This paper conductsa comprehensive assessment and
performance overview of all existing techniques. PSNR, MSE, BER, RMSE, MAE and NPCR these
are the performance measures used to evaluate all different techniques.
Keywords: Applications, Performance Evaluation Metrics, Properties, Reversible Watermarking,
Techniques.
1.

INTRODUCTION

A watermark is a more or less transparent image or text that has been applied to a piece of
paper, another image to either protects the original image, or to make it harder to copy the item e.g.
money watermarks or stamp watermarks.
Reversible watermarking is a data hiding technique. Reversible watermarking restores the
original image without any distortion. It completely recovers the original image from a watermarked
image. This feature is used in medical and military applications, because in these applications of
images do not allow any losses. Reversible Watermarking algorithms are based on lossless
95


compression. Different techniques are used for reversible e.g. Histogram Shifting (HS), Difference
Expansion (DE), Prediction Error Expansion(PEE), Integer Transform, Least Significant Bit (LSB),
Interpolation Technique and Wavelet Transforms.
Histogram shifting (HS) is a useful technique of reversible data hiding. This technique can be
achieved efficiently high capacity and low distortion of an image. Histogram Shifting (HS) is
proposed by ZhichengNi et al. [2] in 2006.
Yongjian Hu [4] proposed a difference-expansion (DE)-based reversible data hiding which is
a new embedding scheme that helps to construct anefficient payload-dependent overﬂow location
map. Difference Expansion (DE) reversiblyembeds a bit in the difference number. It can be Increase
the magnitude of difference. Jun Tian proposed a Difference Expansion technique in 2002. It can be
calculate the differences of neighboring pixel values, and select some difference numbers for
Difference Expansion [4].
Xiaolong Li [6] proposed an efficient reversiblewatermarking scheme based on
AdaptivePrediction-Error Expansion and Pixel Selection. PEE is an important technique of reversible
watermarking which can embed large payloads into digital images with low distortions [6].PEE
technique is proposed by Thodi and Rodriguez in 2007[8]. PEE is an improvement of DE technique.
Because of advantages of PEE are the embedding data with the superior correlating abilities ofa
predictor, resulting in a higher data-embedding capacity than with DE, Histogram modification
framework, Simple, direct and low-complexity, used for lossless compress1ion and it significantly
adds the number of feature elements that expanded for data embedding.
LixinLuo [5] proposed a novel reversible watermarking scheme using an interpolation
technique. Interpolation technique can embed a large amount of convert data into images with
imperceptible modification [5]. In [5] used interpolation error. The difference between interpolation
value and corresponding pixel value, to embed bit “1” or “0” by expanding it additively or leaving it
unchanged. Due to the slight modification of pixels, high image quality is preserved [5].
Sunil Lee [1] proposed a high capacity reversible imagewatermarking scheme based on
integer-to-integer wavelet transforms.ZhichengNi [2] proposed a novel reversible data hiding
algorithm.MehmetUtkuCelik [3] proposed a novel lossless (reversible) LSB data embedding
technique.
Reversible watermarking is useful in remote sensing, military image processing,
medicalimage sharing, multimedia archive management, etc.
2.

PROPERTIES AND APPLICATIONS OF REVERSIBLE WATERMARKING

There are someProperties of Reversible Watermarking as given below. Based on these
properties overall efficiency of reversible watermarking technique can be inspected.
1) Imperceptibility: The watermark should not affect thequality of the original signal, thus it should
be invisible/ inaudible to human eyes/ears.
2) Robustness: The watermark data should not be remove or eliminated by unauthorized distributors,
thus it should be robust to resist common signal processing manipulations such as filtering,
compression, filtering with compression.
3) Capacity: The number of bits that can be embedded in one second of the host signal.
4) Security: The water should only be detected by authorized person.
5) Effectiveness: It is the probability of detection of a watermark immediately after embedding.
6) Fidelity: Perceptual similarity between the original and the watermarked versions of the cover
work.
7) Data Payload: Number of bits a watermark encodes within a unit of time or within a work.
Applications of Reversible Image Watermarking aregiven below,

96


1) Biomedical Imaging techniques: For medical diagnosis, different types of imaging tools such as
X- ray, Ultrasound, computer aided tomography (CT) etc. are used. Ultrasound is widely used a
sound based technique. It measures the differences in echo properties of different organs, and thus
generates images simply by projecting sound into the body, and measuring how and what bounces
back. Computed Tomography is an x-ray based technique developed in the 1970’s and now it is
widely used in medical application. The idea is to take many x-rays of the same slice of body, from
different angles. It is possible to reconstruct x-rays to give a comprehensive image of the slice; this
reconstruction is a complex, Computer based task. MRI uses very strong magnetic fields to
distinguish the different magnetic properties of different forms of tissue - in particular, their water
content. The images that an MR scanner generates can give unrivalled levels of interior details. The
diagrams of X- ray, MRI, Brain, Breast Cancer, Ultrasound and computer aided tomography (CT)
are given below.

Fig.1 MRI and X-ray

Fig.2 Brain and Breast cancer

Fig.3 Ultrasound and computer aided tomography (CT)
2) Remote Sensing: This technique used inflood control, city planning, resource mobilization,
agricultural production monitoring, etc. sensors capture the pictures of the earth’s surface in remote
sensing satellites or multi – spectral scanner, then These pictures are processed by transmitting it to
the Earth station.
97


3) Moving object tracking: This application is used to measure motion parameters and acquire visual
record of the moving object. E.g. Motion based tracking and Recognition based tracking.
4) Automatic Visual Inspection System: This application improves the quality and productivity of
the product in the industries e.g. Automatic inspection of incandescent lamp filaments, Automatic
surface inspection systems and Faulty component identification.
5) Military image processing: This application is used toLong range detection of moving objects
which can be threat is among the success criterion.
3.

RELATED WORK

Mehmet UtkuCelik [7] proposed a novel framework for lossless (invertible) authentication
watermarking, which enables zero-distortionreconstruction of the un-watermarked images upon
verification. Its new framework allows validation of the watermarked imagesbefore recovery of the
original image. It should be increase the image with compressed file size and payload size. The [7]
used the grayscale, 512*512 pixels image for to calculate the efficiency, PSNR and payload size.
Here its values are increased. But there is a problem of losses of images. To solve this loss of an
image the Tsung-Yuan Liu [9] proposed a novel method for generic visible watermarking with a
capability of lossless image recovery. This method can be categorized in two types which are
invisible and visible. This method used the deterministic one-to-one compound mappings of image
pixel values for overlaying a variety of visible watermarksof arbitrary sizes on cover images. Result
can be gives the effectiveness of the proposed approach. This method gives the very low values of
PSNR which is 12-14 dB than [7] method. It gives better recovery of an image. But there is a
problem of distortion of images. To achieve the low distortion SitharaFathima [10] proposed a
transform that introduces lower distortion based on high performance predictor using Median Edge
Detection (MED). The prediction error expansion is calculated for embedding patient information in
the medical image. MED is used to achieve low distortion. This method does not satisfy the
requirement of imperceptibility, capacity and robustness. Then SumalathaLingamgunta[11] proposed
a ‘Reversible Watermarking scheme for Image Authentication’ (RWIA) using Integer Wavelet
Transform that satisfies the requirements of imperceptibility, capacity, and robustness. In [11],
method used Wavelet Tree, Histogram Modification and Watermark Embedding and Extraction to
detect the different attacks.
L. M. Vargas [12] proposed a reversible data hiding algorithm for the capacity problem. It
provides good capacity by exploiting the correlation between neighboring pixels. Its application is
appropriate in medical, cartographic and forensic images because it’s possible to recover the original
image and what’s more the watermarked image is of very good quality so it can be used in some
cases not very demanding. Its applications include authentication, integrity control, or inserting
metadata.
ChaiyapornPanyindee [13] proposed a high performance reversible watermarking technique
which involves adaptable predictor and sorting parameter to suit each image and each payload in
order get lowest image distortion. There is a problem of high distortion and low efficiency of an
image. The [13] has used PEE technique which having small PE values and harmonious PE sorting
parameters will greatly decreases distortion of an image. Genetic algorithm is used to optimize all
parameters and produces the best results possible. The [13] used a gaussian weight function for the
predictor because it can be modifying for specific parameter values by changing only two variables.
The prediction error value cannot be used to sort data because hiding data causes sorting errors when
the decoder attempts to reinterpret the data. It is used the optimization tool to obtain a different
sorting parameters for instead of relying on the prediction error values. It can be produces significant
improvement in an image quality.

98


C. Vinoth Kumar [14] proposed a High Capacity Reversible Data hiding based on histogram
shifting for Medical Images which is used to increase the hiding capacity. It is based on
hierarchically dividing a cover image into smaller blocks for data embedding using the histogram
shifting technique. In this method high data hiding capacity and high stego-image quality are
achieved. But there is a security problem when data can be transmitted. Rhythm Katira [15] proposed
a Random Traversing Based Reversible Data Hiding Technique Using PE and LSB for security of
transmitting data. In [15] proposed methodStenography and LSB, Knight’s tour used. Stenography
makes the data invisible by hiding it in the multimedia such as image, audio or video file and thereby
covering for its existence. Knight’s tour used for increasing the security of data hidden. To increase
the hiding capacity [15] has increase the number of bits embedded but it was reduce the quality of
image, so the proposed method has higher hiding capacity and better image quality.
Anoja C.M [16] proposed a Context Based Reversible Watermarking. This is used to improve
the visual quality of the recover images and to increase the embedding capacity with less
computational complexity and less distortion.Zahra Pakdaman [17] proposed aReversible Image
Watermarking inHadamardDomain to solve the capacity problem. This method does not need any
location map,this property permits to increase the capacity.
A. Nagurammal [18] proposed a generic visible watermarking with a capability of lossless
image recovery for the problem of imperceptibility and robustness of an image. In [18] proposed
method, when image can be converted into HDR image then it can erased image conversion from
normal image. In [18] one-to-one compound mappings used that can map image pixel values to those
of the desired visible watermarks. The algorithm HDR image can be detecting watermarking system
with the requirements of imperceptibility and robustness.
Samira Bouchama [19] proposed a Reversible data hiding scheme for the H.264/AVC video
codec. This technique is developed for the embedding capacity and visual quality of images. In [19]
has used DCT based reversible data hiding method for compressed image to H.264/AVC codec. Here
PSNR can be reduced in dB and increases the bitrate in %. It can be improve the tradeoff between
the embedding capacity, visual quality and the bitrate of the watermarked video. To solve the
security problem
A. Umamageswari [20] proposed a JPEG2000 algorithm and Arnold's cat map
method (Arnold's Transform) to solve the problem ofinformation security of patient’s and increase
the authentication for patient information. In [20] used Region of Interest (ROI) in an image and
trying to embed data in Region of Non Interest (RONI). It can be improve the information security to
maintain a secretly, reliability and accessibility of the embedded data. Here, patient’s information
and disease information is embedded into DICOM images.
4.

PERFORMANCE EVALUATION METRICS

There are some qualities measures used to calculate theperformance evaluation of the
watermarked images which are given below,
1) PSNR (Peak Signal-to-Noise Ratio): PSNR, an abbreviation of Peak Signal-to-Noise Ratio, a term
used to describe objectively the quality of data, which is the result of decompressing encoded data.
PSNR is used to measure the quality of reconstruction of lossy compression (e.g., for image
compression). A higher PSNR value indicates that the reconstruction is of higher quality. It is
calculated by the formula given below,
PSNR= 10*log 10 ቀ

99

ଶହହ‫כ‬ଶହହ
ெௌா

ቁ


2) MSE (Mean Square Error): TheMSE (mean square error) is defined as average squared difference
between a reference image and a distorted image. It is calculated by the formula given below,
ଵ

MSE =௡ ∑௡ ሺ‫ ݅ݔ‬െ ‫݅ݔ‬ሻଶ
௜ିଵ
3) BER (Bit Error Ratio): Itis the ratio that describes how many bits received in error over the
number of the total bits received. It is calculated by comparing bit values of embed and cover image.
BER = P/ (H*W)
4) RMSE (Root mean square error): It can be calculated the quality of fusion watermarked image.
The formula is given below,
RMSE =ට

∑೙ ሺ௬ ௬௜ሻమ
೔సభ ො௜ି
௡

5) MAE (Mean Absolute Error): It can be calculated by the formula is given below,
ଵ

௡௬ିଵ

MAE = ௡௫,௡௬ ‫∑ כ‬௡௫ିଵ ∑଴
଴

|‫ݎ‬ሺ‫ݕ , ݔ‬ሻ െ ‫ݐ‬ሺ‫ݕ ,ݔ‬ሻ|

6) NPCR (Number of Pixel Change Rate): It should be maximum for high quality images. It can be
calculated by the formula is given below,
NPCR =
5.

∑೔,ೕ ஽ ሺ௜,௝ሻ‫כ‬ଵ଴଴%
ௐ‫כ‬ு

DISCUSSION AND OUTCOMES

From the overall study of all different techniques of reversible watermarking we have
discussed that the Histogram Shifting (HS) have very less complexity, they have the lowest
embedding capacity as compared to the other existing techniques. Difference Expansion (DE)
destroyed the location map of completeness, causing mismatching to all the latter pixels [4].
Therefore, the schemes of this type are also fragile under attacks. In Integer Transform, when the
block size is large, the amount of the side information that needs to be embedded is very small.
However, the block size that is too large makes the adaptive embedding less useful especially for
high embedding capacity, and this degrades the performance of this scheme.Theoretical bound for
the embedding capacity is yet to be finding out for number of standard images.
In [6] Prediction Error Expansion (PEE) the capacity of the image is limited up to 1 bit per
pixel. But there are also problems in Prediction Error Expansion (PEE) that the Embedding capacity
is less. I.e. the maximum capacity will be 1 BPP. (As in the prediction error expansion method, the 1
bit of watermark is uniformly embedded into the image pixels whose prediction error comes into the
inner region) [6] after embedding, the quality of image degrades the degradation in the quality of
image degrades due to large amount of shift able pixels. To avoid these problems from Prediction
Error Expansion (PEE) use two new algorithms in AdaptiveDataEmbedding andPixelSelection
methods which are Median edge detector and Gradient Adjusted Prediction (GAP)algorithm. Here
Gradient Adjusted Prediction (GAP) finds the prediction error by using more neighboring pixels [6].

100


6.

CONCLUSION

In this paper we have surveyed the literature of all existing Techniques of Reversible Image
Watermarking for Medical Imaging. Here we have given various aspects for reversible image
watermarking which are introduction, techniques, properties, applications and performance
evaluation metrics. Discussed a brief analysis of reversible watermarking techniques with their
advantages and disadvantages.PSNR, MSE, BER, RMSE, MAE and NPCR these quality measures
calculated the performance evaluation of watermark images which gives better quality image.This
survey paper helps to new researchers to research same area.
7.

ACKNOWLEDGMENT

This research paper is made possible through the help and support from everyone, including:
parents, teachers, family, friends, and in essence, all sentient beings. Especially, please allow me to
dedicate my acknowledgment of gratitude toward the Following significant advisors and
contributors: First and foremost, I would like to extend my gratitude to Hon. Shri Amrish Patel,
Chancellor SVKMs NMIMS for his most support and encouragement by providing excellent
infrastructure and research faculties in Tribal area like Shirpur. I would like to extend my gratitude
to Dr. TapanBagchi, Director Shirpur Campus, Dr M V Deshpande Associate Dean, MPSTME
Shirpur and Dr N S Choubey, Head of the department for constant and unconditional support.
Finally, I sincerely thank to my parents, family, and friends, who provide the advice and financial
support. The product of this research paper would not be possible without all of them.
REFERENCES
[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

Sunil Lee, Chang D. Yoo and TonKalker, “Reversible Image Watermarking Based
onInteger-to-Integer Wavelet Transform”, IEEE TRANSACTIONS ON INFORMATION
FORENSICS AND SECURITY, VOL. 2, NO. 3, SEPTEMBER 2007.
Zhicheng Ni, Yun-Qing Shi, Nirwan Ansari, and Wei Su, “Reversible Data Hiding”, IEEE
TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL.
16, NO. 3, MARCH 2006.
Mehmet UtkuCelik, Gaurav Sharma, Ahmet Murat Tekalp and Eli Saber, “Lossless
Generalized-LSB Data Embedding”, IEEE TRANSACTIONS ON IMAGE PROCESSING,
VOL. 14, NO. 2, FEBRUARY 2005.
Yongjian Hu, Heung-Kyu Lee, and Jianwei Li, “DE-Based Reversible Data Hiding With
Improved Overﬂow Location Map”, IEEE TRANSACTIONS ON CIRCUITS AND
SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 19, NO. 2, FEBRUARY 2009.
LixinLuo, Zhenyong Chen, Ming Chen, Xiao Zeng, and ZhangXiong, “Reversible Image
Watermarking
Using
InterpolationTechnique”,
IEEE
TRANSACTIONS
ON
INFORMATION FORENSICS AND SECURITY, VOL. 5, NO. 1, MARCH 2010.
Xiaolong Li, Bin Yang, and TieyongZeng, “Efﬁcient Reversible Watermarking Based on
Adaptive Prediction-Error Expansion and Pixel Selection”, IEEE TRANSACTIONS ON
IMAGE PROCESSING, VOL. 20, NO. 12, DECEMBER 2011.
Mehmet UtkuCelik, Gaurav Sharma and A. Murat Tekalp, “Lossless Watermarking for
Image Authentication:A New Framework and an Implementation”, IEEE TRANSACTIONS
ON IMAGE PROCESSING, VOL. 15, NO. 4, APRIL 2006.
D. M. Thodi and J. J. Rodriguez, “Expansion embedding techniquesfor reversible
watermarking,” IEEE Trans. Image Process., vol. 16, no.3, pp. 721–730, Mar. 2007.

101


[9]

[10]

[11]

[12]
[13]

[14]

[15]

[16]

[17]

[18]

[19]
[20]

[21]

[22]

[23]

Tsung-Yuan Liu and Wen-Hsiang Tsai, “Generic Lossless Visible Watermarking—ANew
Approach”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 5, MAY
2010.
SitharaFathima, Dr. P. ArockiaJansi Rani and Dr. Dejey, “Authenticating Patient’s Medical
Image using Reversible Watermarking”, International Journal of Emerging Technology and
Advanced Engineering, (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3,
Issue 1, January 2013.
SumalathaLingamgunta, Venkata Krishna Vakulabaranam and SushmaThotakura,
“Reversible Watermarking for Image Authentication using IWT”, International Journal of
Signal Processing, Image Processing and Pattern Recognition Vol. 6, No. 1, February, 2013
L. M. Vargas and E. Vera, “An Implementation of Reversible Watermarking for Still
Images”, IEEE LATIN AMERICA TRANSACTIONS, VOL. 11, NO. 1, FEB.2013.
ChaiyapornPanyindee and ChuchartPintavirooj, “Reversible Watermarking Using Gaussian
Weight Prediction and Genetic Algorithm”, Proceedings of the International
MultiConference of Engineers and Computer Scientists 2013 Vol I, IMECS 2013, March 13
- 15, 2013, Hong Kong.
C. Vinoth Kumar, V. Natarajan and DeepikaBhogadi, “High Capacity Reversible Data
hiding based on histogram shifting for Medical Images”, International conference on
Communication and Signal Processing, April 3-5, 2013, India.
Rhythm Katiracand Prof. V. Thanikaiselvan, “Random Traversing Based Reversible
DataHiding Technique Using PE and LSB”, International Journal of Engineering and
Technology (IJET), Vol 5 No 2 Apr-May 2013.
Anoja C.M and Dr. C. SeldevChirstopher, “Context Based Reversible Watermarking”,
Proceedings of 2013 IEEE Conference on Information and Communication Technologies
(ICT 2013).
Zahra Pakdaman, SaeidSaryazdi, HosseinNezamabadi-pour, “A Reversible Image
Watermarking InHadamardDomain”, 2013 5th Conference on Information and Knowledge
Technology (IKT), 2013 IEEE.
A.Nagurammaland T.Meyyappan, “ Lossless Image Watermarking for HDR Images Using
Tone Mapping”, IJCSNS International Journal of Computer Science and Network Security,
VOL.13 No.5, May 2013.
Samira Bouchama, Hassina Aliane and Latifa Hamami, “Reversible data hiding scheme for
the H.264/AVC codec”, 2013 IEEE.
A. Umamageswari and G.R.Suresh, “Security in Medical Image Communication with
Arnold's Cat map method and Reversible Watermarking”, 2013 International Conference on
Circuits, Power and Computing Technologies [ICCPCT-2013].
Karimella Vikram, Dr. V. Murali Krishna, Dr. Shaik Abdul Muzeer and K. Narasimha,
“Invisible Water Marking Within Media Files using State-of-the-Art Technology”,
International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 3,
2012, pp. 1 - 8, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
Nikhat Nawaz, Syed Saifuddin, Altaf.C, and G.Prasanna Lakshmi, “Visible Watermarking
using Spread Spectrum”, International Journal of Advanced Research in Engineering &
Technology (IJARET), Volume 4, Issue 3, 2013, pp. 108 - 114, ISSN Print: 0976-6480,
ISSN Online: 0976-6499.
Rohini N. Shrikhande and Prof. Vinayak K. Bairagi, “Prediction Based Lossless Medical
Image Compression”, International Journal of Electronics and Communication Engineering
& Technology (IJECET), Volume 4, Issue 2, 2013, pp. 191 - 197, ISSN Print: 0976- 6464,
ISSN Online: 0976 –6472.

102

50120130406010

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (16)

Destaque

Destaque (6)

Semelhante a 50120130406010

Semelhante a 50120130406010 (20)

Mais de IAEME Publication

Mais de IAEME Publication (20)

Último

Último (20)

50120130406010