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 1 January - March 2015

Research Paper

SCR Technique for Tumour Operation using Biomedical Image Processing and its Implementation in TMS320C6713

S. Lysiya Merlin* , M. Nethravathi, K. P. Harini*, N. Santhiyakumari****

__ Student, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem.

**** Professor and Head, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem.

Merlin, S.L., Nethravathi, M., Harini, K.P., and Santhiyakumari, N. (2015). SCR Technique for Tumour Operation using Biomedical Image Processing and its Implementation in TMS320C6713. i-manager’s Journal on Image Processing, 2(1), 1-6. https://doi.org/10.26634/jip.2.1.3261

Abstract

One of the major issues in the field of biomedical research is dealt with operating a tumor tissue successfully without causing any damage to the other good tissues. 'SCR (store, compare, recognize) technique for tumor operation using biomedical image processing and its implementation in TMS320C6713' is an advanced process for identifying and tracking the images of the tumor by using 'Aphelion Dev Software development module'. This is especially used for locating the tumor found in highly sensitive organs like brain and heart. In this project, two stages have been involved. The first stage involves the conversion of raw binary information scanned from a CT scan image of the infected organ to colour image. This processed image helps the surgeons to find the tumor sizes and locate them. In second stage, the operation of the tumor analyzed in stage I is recorded using a camera in a neck worn pendent. The recorded video frames are compared with the stored processed image and its histogram of stage I. As a result of this, the tumor tissues are segmented from the good tissues in the video. Now the output video after processing has only the cropped images of the tumor eliminating the good tissues. Using Parallel Processing technique in TMS320C6713 processor, the computational time can be reduced. Thus the surgeons can operate the identified tumor safely without affecting the other good tissues in a short span of time.

References

[1]. Devesh D. Nawgaje, Dr. R.D. Kanphade, Dr. N.B. Chopade, S. B. Patil, (2010). “White Blood Cell Cancer Detection Using Fuzzy Logic”, CIT Journal of research, Vol 1, No 1, pp 174-182.

[2]. Ashraf, M.; Kim Le; Xu Huang; (2010), “Information gain and adaptive neuro-fuzzy inference system for breast cancer diagnoses”, IEEE, Computer Sciences and Convergence Information Technology (ICCIT), pp. 911–915.

[3]. H. Yoshida, R. Nishikawa, G. Maryellen, and K. Doi, (1996). “Computer-aided diagnosis in mammography: Detection of clustered microcalcifications based on multiscale edge representation,” in Computer Assisted Radiology. Amsterdam, The Netherlands: Elsevier.

[4]. SK Kyriacou, C. Dabatzikos, SJ.zinreich, (2002). ”Non linear elastic recognition of brain images with tumor pathology”, IEEE Transactions on Medical Imaging, Vol 18, No. 7, pp. 67-78.

[5]. Devesh D. Nawgaje, Dr. R.D. Kanphade, (2011). “Implementation of ANFIS for Breast Cancer Detection using TMS320C6713 DSP”, International Journal of Computer Applications, No. 13, Article 2.

[6]. K.Woods, C. Doss, K. Bowyer, J. Solka, C. Priebe, andW. Kegelmeyer,] K.Woods, C. Doss, K. Bowyer, J. Solka, C. Priebe, and W.Kegelmeyer, (1994), “Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography,” Int. J. Pattern Recog. Artif.Intel, Vol. 7, No. 6, pp. 1417–1436.

[7]. Osareh, A.; Shadgar, B (2010), ”Machine learning techniques to diagnose breast cancer”, IEEE Health Informatics and Bioinformatics (HIBIT), pp. 114 – 120.

[8]. Lysiya Merlin S, Santhiyakumari N, (2014), “SCR Technique for biomedical processing using Biomedical image processing”, Indian Technology congress Proceedings.

[9]. H. Yoshida, R. Nishikawa, K. Muto, K. Doi, and M. Tsuda, (1994), “Application of the wavelet transform to automated detection of clustered microcalcifications in digital mammograms,” Tokyo Inst. Polytech., Tokyo, Japan, Academic Rep., Vol. 16.

[10]. Antal, P., Meszaros, T. (2001), Proceedings of fourteenth IEEE Symposium on computer based Medical Systems, July 26-27, Bethesda, MD,177-182.

[11]. Dheeba, J.; Tamil Selvi, S.; (2010), “Screening mammogram images for abnormalities using radial basis Function Neural Network”, IEEE, Communication Control and Computing Technologies (ICCCCT), pp. 554 – 559.

[12]. Nawgaje, Dr. R. D. Kanphade, (2011) “Implementation Of Neural Network For Cancer Diagnosis”, International Journal of Computer Applications, No.1, Article 3, Proceeding of National conference on Information and communication Technology, Nagpur.

[13]. I. S. Torsun, (1995), “Foundation of intelligent knowledge based system”, Academic press; 1 edition.

[14]. G. Schaefer, A. Hassanien, J. Jiang, (2009), “Computational Intelligence in Medical Imaging: Techniques and Applications”, by Chapman and Hall/CRC press

[15].https://www.ticom/sitesearch/docx/universalsearch.t sp?searchterm + TMS320C6713#linkID=17src=top.

[16]. P. Kulkarni and Y. Öztürk, (2007), "Requirements and Design Spaces of Mobile Medical Care," ACM SIGMOBILE Mobile Computing and Communications Review, Vol. 11, pp. 12-30.

[17]. Devesh D. Nawgaje, R. D. Kanphade, (2011), “Implementation of Adaptive Neuro Fuzzy Inference System For Cancer Detection Using TMS320C6711 DSP”, ACM New York, NY, USA ©2011, Proceeding of International Conference and workshop on Emerging Trends and Technology ICWET 2011, Mumbai, pp 608- 612.

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Research Paper

Integrated Registration and Segmentation for Finding Defective Areas in Kidneys

Baby Dhanya S. N* , N. Meffiya, P. Latha*

- PG Scholar, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

Associate Professor, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

Dhanya, S.N.B., Meffiya, N., and Latha, P. (2015). Integrated Registration and Segmentation for Finding Defective Areas in Kidneys. i-manager’s Journal on Image Processing, 2(1), 7-13. https://doi.org/10.26634/jip.2.1.3262

Abstract

Kidney failure is the major threat in medical field. The kidneys dysfunction and disease can be filtered using the parameter Glomerular Filtration Rate(GFR). Finding the affected part is the most critical phenomenon in medical field. This methods gives the exact area detection in kidney so that the diagnosis and treatment is easily carried out. The aim of the project is to find the Glomerular Filtration Rate(GFR) and according to it, the affected part in kidney is found out. It deals with renal failure and it applies the concept of Registration and segmentation and classification .

References

[1]. A. M. Ali, A. A. Farag, and A. S. El-Baz, (2007). “Graph cuts framework for kidney segmentation with prior shape constraints,” in Proc. MICCAI, Vol. 10, pp. 384–392.

[2]. H. Abdelmunim, A. Farag, W. Miller, and M. AboelGhar, (2008). “A kidney segmentation approach from DCE-MRI using level sets,” in Proc. IEEE Comput. Soc. Conf. CVPRW, pp. 1–6.

[3]. J. Milles, R. J. van der Geest, M. Jerosch-Herold, J. H. Reiber, and B. P. Lelieveldt, (2008). “Fully automated motion correction in first-pass myocardial perfusion MR image sequences,” IEEE Trans. Med. Imag., Vol. 27, No. 11, pp. 1611–1621.

[4]. P. Shah and M. D. Gupta, (2012). “Simultaneous registration and segmentation by l1 minimization,” in Machine Learning in Medical Imaging, Vol. 7588, Springer-Verlag, pp. 128–135.

[5]. S. Parisot, H. Duffau, S. Chemouny, and N. Paragios, (2012). “Joint tumor segmentation and dense deformable registration of brain MR images,” in Proc. 15th Int. Conf., MICCAI, Vol. 2, pp. 651–658.

[6]. H. Abdelmunim, A. Farag, W. Miller, and M. AboelGhar, (2008). “A kidney segmentation approach from DCE-MRI using level sets,” in Proc. IEEE Comput. Soc. Conf., CVPRW, pp. 1–6.

[7]. G. Adluru, E. V. DiBella, and M. C. Schabel, (2006). “Model-based registration for dynamic cardiac perfusion MRI,” J. Magn. Reson.Imag., Vol. 24, No. 5, pp. 1062–1070.

[8]. L. Annet, L. Hermoye, F. Peeters, F. Jamar, J.-P.Dehoux, and B. E. V. Beers, (2004). “Glomerular filtration rate: Assessment with dynamic contrast-enhanced MRI and a cortical-compartment model in the rabbit kidney,” J. Magn. Reson. Imag., Vol. 20, No. 5, pp. 843–849.

[9]. G. Aubert and L. Vese, (1997). “A variational method in image recovery,” SIAM J. Numer. Anal., Vol. 34, No. 5, pp. 1948–1979.

[10]. N. J. Bird, C. Peters, A. R. Michell, and A. M. Peters, (2008). “Reproducibilities and responses to food intake of GFR measured with chromium-51-EDTA andiohexol simultaneously and independently in normal subjects,” Nephrol. Dial. Transplant., Vol. 23, pp. 1902–1909.

[11]. A. Böttcher, (2000). “C*-algebras in numerical analysis,” Irish Math. Soc. Bullet., Vol. 45, pp. 57–133.

[12]. W. L. Briggs, V. E. Henson, and S. F. McCormick, (2000). A Multigrid Tutorial. Philadelphia, PA, USA: SIAM.

[13]. G. A. Buonaccorsi et al.,(2007). “Tracer kinetic model-driven registration for dynamic contrastenhanced MRI time-series data,” Magn. Reson. Med., Vol. 58, No. 5, pp. 1010–1019.

[14]. T. F. Chan and L. Vese, (2001). “Active contours without edges, ”IEEE Trans. Image Process., Vol. 10, No. 2, pp. 266–277.

[15]. T. F. Chan and J. Shen, (2005). Image Processing and Analysis: Variational, PDE, Wavelet, and Stochastic Methods. Philadelphia, PA, USA: SIAM.

[16]. H. L. M. Cheng and G. A. Wright, (2006). “Rapid highresolution T1 mapping by variable flip angles: Accurate and precise measurements in the presence of radio frequency field in homogeneity,” Magn. Reson. Med., Vol. 55, No. 3, pp. 566–574.

[17]. E. Krutzén, S. E. Bäck, I. Nilsson-Ehle, and P. Nilsson- Ehle, (1984). “Plasma clearance of a new contrast agent, iohexol: A method for the assessment of glomerular filtration rate,” J. Lab Clin. Med., Vol. 104, No. 6, pp. 955–961.

[18]. A. Yezzi, L. Zöllei, and T. Kapur, (2003). “A variational framework for integrating segmentation and registration through active contours,” Med. Image Anal., Vol. 7, No. 2, pp. 171–185.

[19]. C. Zakkaroff, D. Magee1, A. Radjenovic, and R. Boyle1, (2010). “Mediated spatiotemporal fusion of multiple cardiac magnetic resonance datasets for patient-specific perfusion analysis,” in Proc. Comput. Cardiol., Vol. 37, pp. 469–472.

[20]. J. L. Zhang, H. Rusinek, H. Chandarana, and V. S. Lee,(2013).“Functional MRI of the kidneys,” J. Magn. Reson. Imag., Vol. 37, No. 2, pp. 282–293.

[21] S. T. Wong, Z. Xue, and K. Wong, (2010). “Joint registration and segmentation of serial lung CT images for image-guided lung cancer diagnosis and therapy,” Comput. Med. Imag. Graph., Vol. 34, No. 1, pp. 55–60.

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Research Paper

Bone Thickness Computation at Low Resolution IN-VIVO CT Images and Classification through ANN

Jayachitra* , M. Usha**

* PG Scholar, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.

** Assistant Professor, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.

Jayachitra, S., and Usha, M. (2015). Bone Thickness Computation at Low Resolution IN-VIVO CT Images and Classification through ANN. i-manager’s Journal on Image Processing, 2(1), 14-18. https://doi.org/10.26634/jip.2.1.3263

Abstract

Osteoporosis is a bone disease affecting the bone structure and strength and raising the risk of fractures. Osteoporosis is a bone condition that makes bones thinner and more fragile because of reduced bone density. Osteoporosis may be diagnosed directly through the use of a bone scan that measures bone mineral density (BMD). The micro-architectural quality of Trabecular bone is an important factor of bone quality for evaluating fracture risks under clinical conditions. A new algorithm is implemented for computing TB thickness at a low resolution which is achievable in IN-VIVO images. Here the authors have proposed intercept based algorithm to compute its thickness, and robustness to bone strength. Through this algorithm, the true axis of an object orthogonally intersects a minimum intercept line. By calculating centroids of an image, the thickness is calculated. Detected Image can be classified through artificial neural network classifier.

References

[1]. S. Boonen and A. J. Singer, (2008). “Osteoporosis management: Impact of fracture type on cost and quality of life in patients at risk for fracture I,” Curr. Med. Res. Opin., Vol. 24, No. 6, pp. 1781–1788.

[2]. L. J. Melton, (1997). “Epidemiology of spinal osteoporosis,” Spine, Vol. 22, pp. 2S–11S.

[3]. M. Benito, B. Gomberg, F. W. Wehrli, R. H. Weening, B. Zemel, A. C. Wright, H. K. Song, A. Cucchiara, and P. J. Snyder, (2003). “Deterioration of trabecular architecture in hypogonadal men,” J. Clin. Endocrinol. Metab, Vol. 88, No. 4, pp. 1497–1502.

[4]. A. M. Parfitt, C. H. E. Mathews, A. R. Villanueva, M. Kleerekoper, B. Frame, and D. S. Rao, (1993). “Relationships between surface, volume, and thickness of iliac trabecular bone in aging and in osteoporosis. Implications for the microanatomic and cellular mechanisms of bone loss,” J. Clin. Invest., Vol. 72, No. 4, pp. 1396–1409.

[5]. P. Chavassieux, M. Arlot, and P. J. Meunier, (2001). “Clinical use of bone biopsy,” Osteoporosis, Vol. 2, pp. 501–509.

[6]. T. Hildebrand and P. R¨uegsegger, (1997). “A new method for the model independent assessment of thickness in three-dimensional images,” J. Microscopy, Vol. 185, No. 1, pp. 67–75.

[7]. P. K. Saha and F. W. Wehrli, (2004). “Measurement of trabecular bone thickness in the limited resolution regime of in vivoMRI by fuzzy distance transform,” IEEE Trans. Med. Imag., Vol. 23, No. 1, pp. 53–62.

[8]. A.Vesterby, H. J. Gundersen, and F. Melsen, (1989). “Star volume of marrow space and trabeculae of the first lumbar vertebra: Sampling efficiency and biological variation,” Bone, Vol. 10, No. 1, pp. 7–13.

[9]. P. I. Croucher, N. J. Garrahan, and J. E. Compston, (1996). “Assessment of cancellous bone structure: comparison of strut analysis, trabecular bone pattern factor, and marrow space star volume,” J. Bone Miner. Res., Vol. 11, No. 7, pp. 955–961.

[10]. R. Moreno,M. Borga, and O . Smedby, (2012). “Estimation of trabecular thickness in gray-scale images through granulometric analysis,” Proc. SPIE, Vol. 8314, pp. 831451-8314519.

[11]. P. K. Saha, F. W. Wehrli, and B. R. Gomberg, (2002). “Fuzzy distance transform: Theory, algorithms, and applications,” Comput. Vis. Imag. Understanding, Vol. 86, No. 3, pp. 171–190.

[12]. H, Breu, J. Gil, D. Kirkpatrick, and M. Werman, (1995). “Linear time Euclidean distance transform algorithms,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 17, No. 5, pp. 529–533.

[13]. C. J. Hernandez, G. S. Beaupre, T. S. Keller, and D. R. Carter, (2001). “The influence of bone volume fraction and ash fraction on bone strength and modulus,” Bone, Vol. 29, No. 1, pp. 74–78.

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Research Paper

A Hybrid Robust Watermarking and Encryption for Medical Images using GA Optimized DWT and SVD

Venugopal Reddy* , P. Siddaiah**

* Research Scholar and Professor, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

** Principal, University College of Engineering and Technology, Acharya Nagarjuna University Guntur, Andhra Pradesh, India

Reddy, V., and Siddaiah, P. (2015). A Hybrid Robust Watermarking and Encryption for Medical Images using GA Optimized DWT and SVD. i-manager’s Journal on Image Processing, 2(1), 19-27. https://doi.org/10.26634/jip.2.1.3264

Abstract

Watermarking secures the integrity of the medical images and has been used. One specific limitation for the methods that can be used in water marking medical images is that water marking procedure should not result in degradation of the image. In this work, a dual security approach comprising water marking and encryption is implemented to provide authenticity and protect the integrity of the medical image. A multi objective optimization function that can preserve the structural integrity of the medical image while maintaining robustness and imperceptibility is formulated. Genetic Algorithm (GA) optimization approach is used to optimize the multi objective function. The water mark is implemented using hybrid approach comprising Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The encryption is affected using RSA & AES encryption algorithms. A graphical user interface (GUI) is designed to present the proposed approach in the form of usable tool to help the user in the embedding and extracting process. The water mark is subjected to different types of noise attacks and its robustness and integrity are tested.

References

[1]. S. Boucherkha and M. Benmohamed, (2005). “A lossless watermarking based authentication system for medical images”, International conference on computational intelligence, pp. 240-243.

[2]. P. Meerwald and A. Uhl (2001). “A survey of waveletdomain watermarking algorithms”, Proc of the SPIE security and watermarking of multimedia contents, Vol. 4314, pp. 505–516.

[3]. A. G. Tescher, B. A. Giakoumaki, S. Pavlopoulos, D. Koutsouris (2003). “A medical image watermarking scheme based on wavelet transform”, IEEE EMBS Annual International Conference, Cancun, Mexico, pp. 17–21.

[4]. J. Nayak, P.S. Bhat, R. Acharya, and Niranjan UC, (2004). “Simultaneous storage of medical images in the spatial and frequency domain: a comparative study”, BioMed , Vol. 3, pp. 17.

[5]. Mei Jiansheng , Li Sukang, 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), pp.104-107.

[6]. C. Piao, D. Woo, D. Park, and S. Han, (2008). “ Medical image authentication using hash function and integer wavelet transform”, Congress on Image and Signal Processing, Vol. 1, pp. 7-10.

[7]. A. Kannammal, Dr. S. Subha Rani, (2011). “Authentication of DICOM Medical Images using Multiple fragile watermarking Techniques in Wavelet Transform Domain,” IJCSI International Journal of Computer Science Issues, Vol. 8, No. 6, pp.1694-0814.

[8]. C. S. Woo, J. Du, and B. Pham, (2005). “Multiple watermark method for privacy control and tamper detection in medical images”, WDIC 2005 pages, Australia, pp. 59–64.

[9]. M. Nambakhsh, A. Ahmadian, and H. Zaidi, (2001). “A contextual based double watermarking of PET images by Patient ID and ECG Signal”, Comput Meth. Prog, Vol.104, pp. 341–353.

[10]. A.Kannammal, Dr.S.Subha Rani, (2013). “DICOM medical image watermarking of ECG signals using EZW algorithm Volume”, International Journal of Medical Engineering and Informatics, Vol. 5, pp.126-136.

[11]. A. Kannammal and S. Subha Rani, (2012). “Authentication of Medical Images using Integer Wavelet transforms”, Int J Emerg Technol Adv Eng, Vol. 2, No. 9, pp. 104-108.

[12]. W. Lin, S. Horng, T. Kao, P. Fan, C. Lee, and Y. Pan, (2008). “An efficient watermarking method based on significant difference of wavelet coefficient quantization”, IEEE Trans On Multimedia, Vol. 10, No. 5, pp. 746-757.

[13]. Xinge You, Liang Du, Yiu-ming Cheung, Qiuhui Chen, (2010). “A blind watermarking scheme using new nontensor product wavelet filter banks”, IEEE Transactions on Image Processing, Vol.19, No.12, pp. 3271-84.

[14]. D. Bouslimi, G. Coatrieux, M. Cozic, (2012). ”A joint encryption/watermarking systems for verifying the reliability of medical images”, IEEE Trans Information Technol Biomed, Vol. 16, No. 5, pp. 891-196.

[15]. F. Cao, H. K. Huang, X. Q. Zhou, (2003). “Medical image security in HIPAA mandated PACS environment”, Comput Med Imaging Graphics, Vol. 27, No. 2, pp. 185- 196.

[16]. L. O. M. Kobayashi, S. S. Furuie, and P. S. L. M. Barreto, (2009). “Providing Integrity and Authenticity in DICOM Images: A Novel Approach”, IEEE Trans Inform Technol Biomed, Vol. 13, No. 4, pp. 582-589.

[17]. N. Singhal, J. P. S. Raina, (2011). “Comparative analysis of AES and RC4 algorithms for better utilization”, Int J Comput Trends Technol, pp. 177–181.

[18]. E. Thambiraja, G. Ramesh, and R. Umarani, (2012). “A survey on various most common encryption techniques”, Int J Adv Res Comput Sci Software Eng, Vol. 2, pp. 226–233.

[19]. W. Puech, J.M. Rodrigues, (2004). “A new crypto watermarking method for medical images safe transfer”, Proc. of the 12th European signal processing conference, Vienna, Austria, pp.1481–1484.

[20]. Xin Zhou, Xiaofei Tang, (2011). “Research and Implementation of RSA Algorithm for Encryption and Decryption”, 6th International Forum on Strategic Technology, IEEE, pp.1118 – 1121.

[21]. P. Campisi, A. Neri, M. Visconti, (2001). “A wavelet based method for high frequency subbands watermark embedding,” In Multimedia systems and applications III I, Andrew G. Tescher, Bhaskaran Vasudev, V. Michael Bove, Jr., 344 Editors, Proceedings of SPIE, Vol. 4209.

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Research Paper

Better Scheme for Multimedia Compression using Random Discrete Fractional Fourier Transform for Jpeg 2000 Standard

Deepak Sharma* , Udita Arora, Prakram Suri*, Rishabh Tripathi****

* Research Scholar and Assistant Professor, Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology

-** Student, Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology.

Sharma, D., Arora, U., Suri, P., and Tripathi, R. (2015). Better Scheme for Multimedia Compression using Random Discrete Fractional Fourier Transform for Jpeg 2000 Standard. i-manager’s Journal on Image Processing, 2(1), 28-35. https://doi.org/10.26634/jip.2.1.3265

Abstract

Rapid growth of high quality multimedia (HD) and exchange of data over internet with less storage space and fast processing attracted researchers in the area of compression. Compression is the technique of reducing the image size without degrading the quality of the image. In this work, a commuting matrix with random discrete Fourier transform (DFT) eigenvectors is first constructed. A Random Discrete Fractional Fourier Transform (RDFRFT) kernel matrix with random DFT eigenvectors and eigenvalues is then utilized in image compression. The RDFRFT has an important feature that the magnitude and phase of its transform output are both random. Later, a compression scheme based on random discrete fractional Fourier transform is compared with Discrete Cosine Transform (DCT) and discrete wavelet transform (DWT) based image compression schemes. The given image is subdivided and RDFRFT is applied for each subdivided image to transformed coefficients and reverse order of RDFRFT is applied for reconstruction of original images. The performance of compression scheme based on RDFRFT shows better performance over DFRFT, DCT and DWT based scheme for any multimedia contents. The performance of the proposed scheme is observed on JPEG standard image for prime evaluation parameters such as Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR) for RDFRFT, DCT and DWT based scheme on MATLAB software platform. In addition, the proposed scheme has following advantages: it shows the same computation complexity as DFRFT based system and the feature of additional security can also be incorporated with RDFRFT which is not very significant in case of FRFT and DFRFT based system.

References

[1]. J. S. Lim, (1990). Two-Dimensional Signal and Image Processing, Englewood Cliffs, NJ, Prentice Hall.

[2]. K. R. Rao and P. C. Yip, (2001). “The Transform and Data Compression Handbook”, CRC Press, New York.

[3]. Satish K. Singh and Shishir Kumar, (2010). “Mathematical transforms and image compression: A review” Maejo International Journal of Science and Technology Maejo Int. J. Sci. Technol., Vol. 4, No. 2, pp. 235-249.

[4]. V. Namias, (1980). “The fractional order Fourier transform and its application to quantum mechanics,” J. Inst. Math. Appl., Vol. 25, No. 3, pp. 241–265.

[5]. L. B. Almeida, (1994). “The fractional Fourier transform and time-frequency representations,” IEEE Trans. Signal Processing, Vol. 42, No. 11, pp. 3084–3091.

[6]. S. C. Pei and M. H. Yeh, (1997). “Improved discrete fractional Fourier transform,” Opt. Lett., Vol. 22, No. 14, pp. 1047–1049.

[7]. C. Candan, M. A. Kutay, and H. M. Ozaktas, (2000). “The discrete fractional Fourier transform,” IEEE Trans. Signal Process., Vol. 48, No. 5, pp. 1329–1337.

[8]. S. C. Pei and W. L. Hsue, (2006). “The multipleparameter discrete fractional Fourier transform,” IEEE Signal Process. Lette., Vol. 13, No. 6, pp. 329–332.

[9]. B. W. Dickinson and K. Steiglitz, (1982). “Eigenvectors and functions of the discrete Fourier transform,” IEEE Trans. Acoust., Speech, Signal Process., Vol. ASSP-30, pp. 25–31.

[10]. Z. Liu and S. Liu, (2007). “Randomization of the Fourier transform,” Opt. Lett., Vol. 32, pp. 478–480.

[11]. S. C. Pei, M. H. Yeh, and C. C. Tseng, (1999). “Discrete fractional Fourier transform based on orthogonal projection,” IEEE Trans. Signal Processing, Vol. 47, No. 5, pp. 1335–1348.

[12]. Soo-Chang Pei and Wen-Liang Hsue, (2009). “Random Discrete Fractional Fourier Transform”, IEEE Signal Processing Letters, Vol. 16, No. 12, pp. 1015-1018.

[13]. Prabhakar.Telagarapu, V.Jagan Naveen, A.Lakshmi..Prasanthi, G.Vijaya Santhi, (2011). “Image Compression Using DCT and Wavelet Transformations” International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 4, No. 3, pp. 61- 74.

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

Current Issue Vol. 11 Issue 3

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Volume 2 Issue 1 January - March 2015

SCR Technique for Tumour Operation using Biomedical Image Processing and its Implementation in TMS320C6713

S. Lysiya Merlin* , M. Nethravathi**, K. P. Harini***, N. Santhiyakumari****

*_**_*** Student, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem. **** Professor and Head, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem.

Merlin, S.L., Nethravathi, M., Harini, K.P., and Santhiyakumari, N. (2015). SCR Technique for Tumour Operation using Biomedical Image Processing and its Implementation in TMS320C6713. i-manager’s Journal on Image Processing, 2(1), 1-6. https://doi.org/10.26634/jip.2.1.3261

Abstract

References

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Integrated Registration and Segmentation for Finding Defective Areas in Kidneys

Baby Dhanya S. N* , N. Meffiya**, P. Latha***

*-** PG Scholar, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India. *** Associate Professor, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

Dhanya, S.N.B., Meffiya, N., and Latha, P. (2015). Integrated Registration and Segmentation for Finding Defective Areas in Kidneys. i-manager’s Journal on Image Processing, 2(1), 7-13. https://doi.org/10.26634/jip.2.1.3262

Abstract

References

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Bone Thickness Computation at Low Resolution IN-VIVO CT Images and Classification through ANN

Jayachitra* , M. Usha**

* PG Scholar, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur. ** Assistant Professor, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.

Jayachitra, S., and Usha, M. (2015). Bone Thickness Computation at Low Resolution IN-VIVO CT Images and Classification through ANN. i-manager’s Journal on Image Processing, 2(1), 14-18. https://doi.org/10.26634/jip.2.1.3263

Abstract

References

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A Hybrid Robust Watermarking and Encryption for Medical Images using GA Optimized DWT and SVD

Venugopal Reddy* , P. Siddaiah**

* Research Scholar and Professor, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India ** Principal, University College of Engineering and Technology, Acharya Nagarjuna University Guntur, Andhra Pradesh, India

Reddy, V., and Siddaiah, P. (2015). A Hybrid Robust Watermarking and Encryption for Medical Images using GA Optimized DWT and SVD. i-manager’s Journal on Image Processing, 2(1), 19-27. https://doi.org/10.26634/jip.2.1.3264

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Better Scheme for Multimedia Compression using Random Discrete Fractional Fourier Transform for Jpeg 2000 Standard

Deepak Sharma* , Udita Arora**, Prakram Suri***, Rishabh Tripathi****

* Research Scholar and Assistant Professor, Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology **-**** Student, Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology.

Sharma, D., Arora, U., Suri, P., and Tripathi, R. (2015). Better Scheme for Multimedia Compression using Random Discrete Fractional Fourier Transform for Jpeg 2000 Standard. i-manager’s Journal on Image Processing, 2(1), 28-35. https://doi.org/10.26634/jip.2.1.3265

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S. Lysiya Merlin* , M. Nethravathi, K. P. Harini*, N. Santhiyakumari****

__ Student, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem.

**** Professor and Head, Department of Electronics and Communication Engineering, Knowledge Institute of Technology, Salem.

Baby Dhanya S. N* , N. Meffiya, P. Latha*

- PG Scholar, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

Associate Professor, Department of Computer Science and Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.

* PG Scholar, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.

** Assistant Professor, Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur.

* Research Scholar and Professor, Acharya Nagarjuna University, Guntur, Andhra Pradesh, India

** Principal, University College of Engineering and Technology, Acharya Nagarjuna University Guntur, Andhra Pradesh, India

Deepak Sharma* , Udita Arora, Prakram Suri*, Rishabh Tripathi****

* Research Scholar and Assistant Professor, Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology

-** Student, Department of Electronics and Communication Engineering, Jaypee University of Engineering & Technology.