i-manager Publications

i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 11 Issue 3

Most Cited

Identification of Volcano Hotspots by using Resilient Back Propagation (RBP) Algorithm Via Satellite Images
Data Hiding in Encrypted Compressed Videos for Privacy Information Protection
Improved Video Watermarking using Discrete Cosine Transform
Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption
Denoising of Images by Wavelets and Contourlets using Bi-Shrink Filter

Volume 2 Issue 3 July - September 2015

Research Paper

Transform Based Efficient and Robust Watermarking Technique for Medical Images Using Differential Evolution

Venugopal Reddy* , P. Siddaiah**

* Professor, Department of Electronics and Communication Engineering, Nalanda Institute of Engineering and Technology, Guntur, A.P, India.

** Professor and Dean, Department of Electronics and Communication Engineering, University College of Engineering and Technology, Acharya Nagarjuna University, Guntur, A.P, India.

Reddy, V., and Siddaiah, P. (2015). Transform Based Efficient and Robust Watermarking Technique for Medical Images Using Differential Evolution. i-manager’s Journal on Image Processing, 2(3), 1-13. https://doi.org/10.26634/jip.2.3.3600

Abstract

In this research work, a transform based medical image watermarking algorithm is used. Differential Evolution (DE) optimization technique is proposed to ensure that the watermark maintains its structural integrity along with robustness and imperceptibility. Differential Evolution (DE) optimization is employed to optimize the objective function to choose a correct type of wavelet and scaling factor. The water marking is proposed to be implemented using Discrete Wavelet Transforms (DWT), Lifted Wavelet Transform (LWT) and Singular Value Decomposition (SVD) techniques. The encryption is done using RSA and AES encryption algorithms. A Graphical User Interface (GUI) which enables the user to have ease of operation in loading the image, watermark it, encrypt it, and also retrieve the original image whenever necessary is also designed and presented in this paper. The robustness and the integrity of the watermark are tested by measuring different performance parameters and subjecting it to various attacks.

References

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[7]. Pal, K., G. Ghosh and M. Bhattacharya. (2012). “Biomedical Image Watermarking in Wavelet Domain for Data Integrity Using Bit Majority Algorithm and Multiple Copies of Hidden Information”, American Journal of Biomedical Engineering, Vol.2, No.2, pp- 29-37.

[8]. X. Luo, Q. Chanag, J. Tun. (2003). "A Lossless Data Hiding Scheme for Medical Images in Application of ediagnosis", Proceedings of the 12th Annual International Conference of IEEE, EMBS, Cancum, Mexico, pp.17-21.

[9]. Y. Wang, J. F. Doherty and R. E. Van Dyck. (2000). “A Wavelet-Based Watermarking Algorithm for Ownership Verification of Digital Images”, IEEE Transactions on Image Processing, Vol.11, No. 2, pp.77-88.

[10]. C. C. Lai, C. C. Tsai, (2010). "Digital image watermarking using discrete wavelet transform and singular value decomposition", IEEE Trans . Instrumentation and Measurement, Vol. 59, No. 11, pp. 3060-3067.

[11]. M. Jiansheng, L. Sukang, and Tan Xiaomei, (2009). “A digital watermarking algorithm based on DCT and DWT”, Proceedings of the 2009 International Symposium on Web Information Systems and Applications (WISA '09), Nanchang, PR China, pp. 104–107.

[12]. Li, Qiang, Chun Yuan, and Yu-ZhuoZhong., (2007). "Adaptive DWT-SVD domain image watermarking using human visual model", Advanced Communication Technology, The 9th International Conference, Vol. 3. IEEE.

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[15]. N. Venkatram, L.S.S.Reddy, P.V.V.Kishore, (2014). ” Blind Medical Image Watermarking with LWT – SVD for Telemedicine Applications”, Wseas Transactions On Signal Processing, Vol.10, pp-288-300.

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Research Paper

Optical Realization for Tissue Imaging

K. Rajkumar* , V. Padmaja**

* Associate Professor, SR Engineering College, Warangal, Telangana, India.

** Professor, VNR VJIET, Hyderabad, Telangana, India.

Rajkumar, K., and Padmaja, V. (2015). Optical Realization for Tissue Imaging. i-manager’s Journal on Image Processing, 2(3), 14-18. https://doi.org/10.26634/jip.2.3.3601

Abstract

Polarimetric imaging is an emerging technique that uses polarized light to probe the acceptance of the tissue. Basically, tissues are mostly accepted by the clinicians by their chemical processing which takes some time to get the results. Polarization imaging techniques are utilized to understand the variation of polarization properties of optical light which passes through the tissue. The obtained images that are obtained when optical light passes through tissue helps the clinicians to understand and assess the tissue properties and provide knowledge about the functioning of the tissue. In this work, the tissues collected are used for the experimental purpose of tissue interaction with the optical light and is used whether the imaging results of this experiment can be used to study its properties by the signature matrix of the tissue. If the recognizable Mueller Matrix is obtained from this process with rotating optics, then we can understand the polarization variation in the tissue through a novel pixel by pixel analysis technique and via the normalized signature matrix of the tissue.

References

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[6]. Kiyoshi Lchimoto, Kasuya Shinoda, Tetsuya y amamoto and Junko Kiyohara.(2006). “Photopolarimetric measurement system of Mueller matrix with dual rotating waveplates”. National Astronomical Observation, Japan, Vol. 9, pp. 11- 19.

[7]. Edward Collette (1993). Polarized Light Fundamentals and Applications, Chapter 15, Marcel Dekker, New York.

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[12]. R. Srinivasan, D. Kumar and M. Singh, (2004). “Optical characterization and imaging of biological tissues”, Curr. Science, Vol. 87, pp.218-228.

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[15]. 3. V. V. Tuchin (1994). Selected Papers on Tissue Optics: Applications in MedicalDiagnostics and Therapy,(eds), Vol. MS102, SPIE Press, Bellingham, WA.

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Research Paper

Comparative Analysis of BM3D and Complex Wavelet Transform based Image Denoising Techniques

S. Swarnalatha* , P. Satyanarayana, B. Shoban Babu*

* Associate Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.

** Professor, Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India.

Swarnalatha, S., Satyanarayana, P., and Babu, B.S. (2015). Comparative Analysis of BM3D and Complex Wavelet Transform based Image Denoising Techniques. i-manager’s Journal on Image Processing, 2(3), 19-28. https://doi.org/10.26634/jip.2.3.3602

Abstract

This paper presents a comparison between the BM3D and Complex Wavelet Transform based image denoising techniques based on their performance analysis. Complex Wavelet Transforms overcome the limitations of classic Discrete Wavelet Transforms such as, shift sensitivity, poor directionality. Block Matching with 3D filtering (BM3D) technique is a combination of spatial and transform domain filtering techniques. BM3D employs the spatial filtering like Wiener Filtering, and transform based techniques such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT); and fusing all the filtered results into a single image to accomplish better performance. As the BM3D techniques use both the spatial and transform based filtering techniques, it achieves a better performance than that of the Complex Wavelet Transforms. However, BM3D based image denoising technique consumes more time than that of Wavelet Transform based image denoising techniques.

References

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[6]. I. Firoiu, A. Isar, and D. Isar, (2009). A maximum A Posteriori Approach of Hyperanalytic Wavelet Based Image Denoising in a Multi-Wavelet Context”, Proc of the 9th WSEAS International Conference on Signal Processing, pp. 113-119.

[7]. N. Kingsbury, (2001). Complex Wavelets for Shift Invariant Analysis and Filtering of Signals, Applied and Comp. Harm. Anal.10, pp. 234-253.

[8]. N G Kingsbury, (2000). “A Dual-Tree Complex Wavelet Transform with improved orthogonality and symmetry properties”, Proc. IEEE Conf. on Image Processing, Vancouver, Vol. 2, pp. 375-378.

[9]. Ivan W. Selesnick (2001). “The Characterization and Design of Hilbert Transform Pairs of Wavelet Bases”. Conference on Information Science and Systems, The Johns Hopkins University.

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[11]. Ivan W. Selesnick, Richard G. Baraniuk and Nick G. Kingsbury (2005). “The Dual-Tree Complex Wavelet Transform”, IEEE Signal Processing Magazine, Vol. 22, No. 6, pp. 123-151.

[12]. C. Nafornita, I. Firoiu, J. Boucher, A. Isar, (2008). “A New Watermarking Method Based on the use of the Hyperanalytic Wavelet Transform”, Proc. SPIE 7000, Optical and Digital Image Processing.

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[14]. S. Swarnalatha, P. Satyanarayana, B. Shoban Babu, (2015). “Image Denoising Techniques Using Dual Tree Complex and Hyperanalytic Wavelet Transform With Bi- Shrink Filter”, IJAREEIE, Vol. 4, No. 4, pp. 2572-2580.

[15]. S. Swarnalatha, P. Satyanarayana, B. Shoban Babu, (2015). “Hyperanalytic Dual Tree Complex Wavelet Transform Based Image Denoising Using Bi-Variate Shrinkage Filter”, IJMRA, Vol. 3, No. 8, pp. 102-121.

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Research Paper

Improved Video Watermarking using Discrete Cosine Transform

Dolley Shukla* , Manisha Sharma**

*Associate Professor, Department of Electronics and Telecommunication Engineering, ShriShankaracharya College of Engineering and Technology, Bhilai, India.

** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.

Shukla, D., and Sharma, M. (2015). Improved Video Watermarking using Discrete Cosine Transform. i-manager’s Journal on Image Processing, 2(3), 29-34. https://doi.org/10.26634/jip.2.3.3603

Abstract

In this paper, an improved video watermarking algorithm is presented for copy protection. Each video frame is divided into non-overlapping 8*8 blocks. Each block is a transformed 2D-DCT. After performing the same transformation to all the blocks and frames, embedding of image watermarking is performed with the selected DCT coefficients. Performance of the system is evaluated using peak Signal to Noise Ratio, Mean Square Error, Normalized Mean Square Error, Root Mean Square Error and Absolute Mean Error.

References

[1]. C.I. Podilchuk, and E.J. Delp (2001). “Digital watermarking: algorithms and applications”, Signal Processing Magazine, IEEE, Vol. 18, pp.33-46.

[2]. Potdar, V. M., Han, S., and Chang, E.,(2005). "A Survey rd of Digital Image Watermarking Techniques", 3 IEEE International Conference on Industrial Informatics (INDIN), pp. 709-716.

[3]. Yeo and M.M. Yeung, (1997). “Analysis and synthesis for new digital video applications”, International Conference on Image Processing (ICIP'97), Vol. 1, pp.1.

[4]. SmithaRao, Jyothsna A. N, and Pinaka Pani. R, (2012). “Digital watermarking: applications, techniques and attacks”, International Journal of Computer Applications, Vol. 44, No. 7, pp. 29-34.

[5]. M. Natarajan, and G. Makhdumi (2009). “ Safeguarding the Digital Contents: Digital Watermarking,” DESIDOC Journal of Library & Information Technology, Vol. 29, pp. 29-35.

[6]. G. Doërr, and J.L. Dugelay, (2004). “Security Pitfalls of Frame-by-Frame Approaches to Video Watermarking”, Signal Processing, IEEE Transactions, Vol. 52, pp. 2955 – 2964.

[7]. M. K. Thakur, V. Saxena, and J. P. Gupta, (2010). “A Performance Analysis of Objective Video Quality Metrics for Digital Video Watermarking”, Computer Science and Information Technology (ICCSIT), 2010, 3rd IEEE International Conference, Vol. 4, pp. 12- 17.

[8]. Manik Mondal, and Debalina Barik, (2012). “Spatial domain robust watermarking scheme for color image”, International Journal of Advanced Computer Science, Vol.2, No.1,pp. 24-27.

[9]. T. Tokar, T. Kanocz, and D. Levicky, (2009). “Digital watermarking of uncompressed video in spatial domain,” 9th International Conference on Radioelectronica, IEEE, pp. 319-322.

[10]. Rao K, and P. Yip, (1990). Discrete Cosine Transform: algorithms, advantages, applications, Academic press, USA.

[11]. S. Feng, D. Lin, S. C. Shie, and J. Y. Guo, (2010). “Improving the robustness of DCT based image watermarking against JPEG compression”, Computer Standards & Interface, Vol. 32, pp. 54–60.

[12]. J. R. Hemandez, M. Amado, (2000). “DCT domain watermarking techniques for still images as detector performance analysis and a new structure,” IEEE Transactions on Image Processing, Vol. 9, pp. 55-68.

[13]. Kothari A., Dwivedi V.V. (2011). “Performance Analysis of Digital Video Watermarking using Discrete Cosine Transform”, International Journal of Electrical and Computer Engineering Systems, Vol. 2, No. 1.

[14]. Lu Jianfeng; Yang Zhenhua; Yang Fan; Li li, (2011). “A MPEG2 Video Watermarking Algorithm Based on DCT Domain”, Institute of Computer Graphics and Image Processing IEEE Computer Society.

[15]. R. Eswaraiah and E. Sreenivas a Reddy, (2012). “Robust Watermarking Method for Color Images Using DCT Coefficients of Watermark”, Global Journals Inc(US), Vol. 12, No. 12.

[16]. Dr. K. Ramanjaneyulu , Dr. P. Pandarinath and B. Rakesh Reddy, (2013). “Robust and Oblivious Watermarking based on Swapping of DCT Coefficients”, International Journal of Application or Innovation in Engineering & Management, Vol. 2, No. 7.

[17]. Masoumi, M., Amiri, S., (2012). "A High Capacity Digital Watermarking Scheme for Copyright protection of Video Data based on YCbCr Color Channels Invariant to Geometric and Non-Geometric Attacks", International Journal of Computer Applications, Vol.51, No.13, pp. 0975-8887.

[18]. Wood H., (2013). “Invisible digital watermarking in the spatial and dct domains for color images”, International Journal of Scientific and Research Publications, Vol. 3, No. 4.

[19]. Gopika V Mane*, G. G. Chiddarwar, (2013). “Review Paper on Video Watermarking Techniques”, International Journal of Scientific and Research Publications, Vol. 3, No. 4, pp. 1-5 .

[20]. Priya Porwal, Tanvi Ghag, Nikita Poddar, Ankita Tawde, (2014). “Digital video watermarking using modified lsb and dct technique”, IJRET: International Journal of Research in Engineering and Technology, IJRET, Vol. 03, No. 04.

[21]. Jaya Jeswani, Dr. Tanuja Sarode, (2014). “An Improved Blind Color Image Watermarking Using DCT in RGB Color Space”, International Journal of Computer Applications (IJCA), Vol. 92, No. 14.

[22]. Jaya Jeswani, Dr. Tanuja Sarode, (2014). “A New DCT based Color Video Watermarking using Luminance Component”, IOSR Journal of Computer Engineering (IOSR-JCE) ,Vol.16, No. 2, pp. 83-90.

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Research Paper

Denoising Technique for Segmentation of Medical Images using Biorthogonal Wavelet Transform

Ritu Agrawal* , Manisha Sharma**

* Research Scholar, Chhattisgarh Swami Vivekananda Technical University, Bhilai, India.

** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.

Agrawal, R., and Sharma, M. (2015). Denoising Technique for Segmentation of Medical Images using Biorthogonal Wavelet Transform. i-manager’s Journal on Image Processing, 2(3), 35-39. https://doi.org/10.26634/jip.2.3.3604

Abstract

The proposed paper is divided into two parts: firstly image denoising and secondly image segmentation. All medical images are corrupted by noise at the time of transmission and acquisition. The goal of denoising is to remove noise while retaining the visual quality of image. Biorthogonal wavelet transform via hard thresholding is used for denoising. The second part of the paper is designed for segmentation of Magnetic Resonance Angiography (MRA) cardio image after denoising. Image segmentation plays a significant role in medical field. The aim of image segmentation is to extract meaningful objects lying in the image. The analysis of MRA images using two segmentation methods namely region growing method and fuzzy c- means method are discussed in the presence of different noise level and comparison of two segmentation techniques based on accuracy has been performed. The experimental results shows that fuzzy cmeans method yields better segmentation result compared to the region growing method.

References

[1]. Dong, G. and XieM. (2005). “Color clustering and learning for image segmentation based on neural networks”, IEEE Transaction on Neural Networks, Vol.16, pp. 925-936.

[2]. Khare, A., and Tiwary,U. S.(2005). “Soft-Thresholding for denoising of medical images A Multiresolution Approach”, International Journal of Wavelets, Multi resolution and Information Processing, Vol. 3, pp. 477–496.

[3]. Egiazarian,K., and Astola,J. (1999).“New Algorithm for Removing of Mixed (White and Impulsive) Noise from Images”, Nonlinear Image Processing X, Proceedings of SPIE, Vol. 36, No. 46, pp. 78-89.

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[5]. Forouzanfar,M., Abrishami-MoghaddamH., and GhadimiS (2008). “Locally adaptive multiscale Bayesian method for image denoising based on bivariate normal inverse Gaussian distributions,” International Journal of Wavelets, Multiresolution and Information Processing, Vol. 6, pp. 653-664.

[6]. Mallat, S.(1998). “A Wavelet Tour of Signals Processing”, Academic Press, London

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i-manager's Journal on Image Processing (JIP)

Current Issue Vol. 11 Issue 3

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Volume 2 Issue 3 July - September 2015

Transform Based Efficient and Robust Watermarking Technique for Medical Images Using Differential Evolution

Venugopal Reddy* , P. Siddaiah**

Reddy, V., and Siddaiah, P. (2015). Transform Based Efficient and Robust Watermarking Technique for Medical Images Using Differential Evolution. i-manager’s Journal on Image Processing, 2(3), 1-13. https://doi.org/10.26634/jip.2.3.3600

Abstract

References

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Optical Realization for Tissue Imaging

K. Rajkumar* , V. Padmaja**

* Associate Professor, SR Engineering College, Warangal, Telangana, India. ** Professor, VNR VJIET, Hyderabad, Telangana, India.

Rajkumar, K., and Padmaja, V. (2015). Optical Realization for Tissue Imaging. i-manager’s Journal on Image Processing, 2(3), 14-18. https://doi.org/10.26634/jip.2.3.3601

Abstract

References

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Comparative Analysis of BM3D and Complex Wavelet Transform based Image Denoising Techniques

S. Swarnalatha* , P. Satyanarayana**, B. Shoban Babu***

* Associate Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India. ** Professor, Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India.

Swarnalatha, S., Satyanarayana, P., and Babu, B.S. (2015). Comparative Analysis of BM3D and Complex Wavelet Transform based Image Denoising Techniques. i-manager’s Journal on Image Processing, 2(3), 19-28. https://doi.org/10.26634/jip.2.3.3602

Abstract

References

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Improved Video Watermarking using Discrete Cosine Transform

Dolley Shukla* , Manisha Sharma**

*Associate Professor, Department of Electronics and Telecommunication Engineering, ShriShankaracharya College of Engineering and Technology, Bhilai, India. ** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.

Shukla, D., and Sharma, M. (2015). Improved Video Watermarking using Discrete Cosine Transform. i-manager’s Journal on Image Processing, 2(3), 29-34. https://doi.org/10.26634/jip.2.3.3603

Abstract

References

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Denoising Technique for Segmentation of Medical Images using Biorthogonal Wavelet Transform

Ritu Agrawal* , Manisha Sharma**

* Research Scholar, Chhattisgarh Swami Vivekananda Technical University, Bhilai, India. ** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.

Agrawal, R., and Sharma, M. (2015). Denoising Technique for Segmentation of Medical Images using Biorthogonal Wavelet Transform. i-manager’s Journal on Image Processing, 2(3), 35-39. https://doi.org/10.26634/jip.2.3.3604

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* Associate Professor, SR Engineering College, Warangal, Telangana, India.

** Professor, VNR VJIET, Hyderabad, Telangana, India.

S. Swarnalatha* , P. Satyanarayana, B. Shoban Babu*

* Associate Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.

** Professor, Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, India.

*Associate Professor, Department of Electronics and Telecommunication Engineering, ShriShankaracharya College of Engineering and Technology, Bhilai, India.

** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.

* Research Scholar, Chhattisgarh Swami Vivekananda Technical University, Bhilai, India.

** Professor and Head, Department of Electronics and Telecommunication, Bhilai Institute of Technology, Durg, India.