Home > CSC-OpenAccess Library > Manuscript Information
EXPLORE PUBLICATIONS BY COUNTRIES |
EUROPE | |
MIDDLE EAST | |
ASIA | |
AFRICA | |
............................. | |
United States of America | |
United Kingdom | |
Canada | |
Australia | |
Italy | |
France | |
Brazil | |
Germany | |
Malaysia | |
Turkey | |
China | |
Taiwan | |
Japan | |
Saudi Arabia | |
Jordan | |
Egypt | |
United Arab Emirates | |
India | |
Nigeria |
A Novel and Efficient Lifting Scheme based Super Resolution Reconstruction for Early Detection of Cancer in Low Resolution Mammogram Images
Liyakathunisa, C.N .Ravi Kumar
Pages - 53 - 75 | Revised - 01-05-2011 | Published - 31-05-2011
MORE INFORMATION
KEYWORDS
Mammograms, Lifting Wavelet Transform, Super Resolution, Adaptive Interpolation, Soft Thresholding
ABSTRACT
Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, are difficult to be detected because mammograms are low contrast and noisy images. We concentrate on a special case of super resolution reconstruction for early detection of cancer from low resolution mammogram images. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. This paper describes a novel approach for enhancing the resolution of mammographic images. We are proposing an efficient lifting wavelet based denoising with adaptive interpolation for super resolution reconstruction. Under this frame work, the digitized low resolution mammographic images are decomposed into many levels to obtain different frequency bands. We use Daubechies (D4) lifting schemes to decompose low resolution mammogram images into multilevel scale and wavelet coefficients. Then our proposed novel soft thresholding technique is used to remove the noisy coefficients, by fixing optimum threshold value. In order to obtain an image of higher resolution adaptive interpolation is applied. Our proposed lifting wavelet transform based restoration and adaptive interpolation preserves the edges as well as smoothens the image without introducing artifacts. The proposed algorithm avoids the application of iterative method, reduces the complexity of calculation and applies to large dimension low-resolution images. Experimental results show that the proposed approach has succeeded in obtaining a high-resolution mammogram image with a high PSNR, ISNR ratio and a good visual quality.
1 | Rao, B. J., & Krishna, O. V. Lifting Wavelet Transform for Super Resolution Image Reconstruction using matlab. cvr journal of science & technology, 42. |
2 | Padavala, S. (2012). Super Resolution Image Reconstruction using LWT (Doctoral dissertation, Blekinge Institute of Technology). |
1 | Google Scholar |
2 | Academic Journals Database |
3 | CiteSeerX |
4 | refSeek |
5 | iSEEK |
6 | Libsearch |
7 | Bielefeld Academic Search Engine (BASE) |
8 | Scribd |
9 | WorldCat |
10 | SlideShare |
11 | PdfSR |
A.Jensen, A.la Cour-Hardo, “Ripples in Mathematics” , Springer publications. | |
Alexander wong and Jacob Scharcanski, “ Phase -adaptive Super Resolution of mammogram Images using complex wavelts ", 2009. | |
B. Girod, “What’s wrong with mean-squared error?” in Digital images and human vision, MIT Press, Cambridge, MA. ISBN 0-262-23171-9, pp. 207–220, 1993. | |
Barbara ZItova, J.Flusser, “Image Registration: Survey”, Image and vision computing, 21, Elsevier publications, 2003. | |
D. Gnanadurai, V.Sadsivam, “An Efficient Adaptive Threshoding Technique for Wavalet based Image Denosing” , IJSP, Vol 2, spring 2006. | |
D.L.Donoho and I.M JohnStone, “Adapting to unknown smoothness via wavelet shrinkage “, Journal of American Association, Vol 90,no, 432, pp1200-1224 . | |
E.D. Castro, C. Morandi, “Registration of translated and rotated images using finite Fourier transform” , IEEE Transactions on Pattern Analysis and Machine Intelligence 700–703, 1987. | |
G.K. Lemanur, Drocihe and J.Decoinck, “Highly Regular Wavelets for the Detection of clustered Micro calcification in Mammograms”, IEEE Trans on Medical Imaging, VOl.22, No. 3, March 2003. | |
G.R.Ayer and J.C.Danity, “ Iterative Blind Deconvolution”, vol13, No 7, optics Letters, July 1988. | |
Gonazalez Woods , “ Digital Image Processing”, 2nd Edition. | |
H. Greenspan, G. Oz, N. Kiryati, and S. Peled, “Super-resolution in mri,” in Proceedings of IEEE International Symposium on Biomedical Imaging, pp. 943 -946, 2002. | |
Huang, X.S., Chen, Z, “A Wavelet-Based Image Fusion Algorithm" , In Proceedings of the IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering (TENCON 2002), 602-605, Beijing 2002. | |
I. B´egin and F. P. Ferrie, “Comparison of super-resolution algorithms using image quality measures,” in Proceedings of the 3rd Canadian conference on computer and robot vision. Washington, DC, USA: IEEE Computer Society, pp. 72, 2006. | |
J. A. Kennedy, O. Israel, A. Frenkel, R. Bar-Shalom, and H. Azhari, “Super-resolution in pet imaging”, IEEE transactions on medical imaging, vol. 25, no. 2, pp. 137 - 147, February 2006. | |
J. T. Hsu1, C. C. Yen, C. C. Li, M. Sun, B. Tian, and M. Kaygusuz, “Application of waveletbased pocs super resolution for cardiovascular mri image enhancement ”, in Proceedings of the Third International Conference on Image and Graphics (ICIG04), pp. 572-575, 2004. | |
J.Kristin , McLoughlin, J.Philip , Bones, “Noise Equalization for Detection of Micro calcification Clusters in Direct Digital Mammogram Images”, IEE Trans on Medical Imaging, Vol 23, No 0 , March 2004. | |
Jun Zhang, olacFuentes and Ming-Yingleung, “Super resolution of Mammograms", 2010. | |
Liyakathunisa and C.N.Ravi Kumar, “A Novel and Robust Wavelet Based Super Resolution Reconstruction of Low Resolution Images Using Efficient Denoising and Adaptive Interpolation”, in International Journal of Image Processing-IJIP, CSC Journals Publications, Issue 4, Vol 4, pp 401-420, 2010. | |
Liyakathunisa and C.N.Ravi Kumar, “Advances in Super Resolution Reconstruction of Low Resolution Images" International Journal of Computational Intelligence Research ISSN 0973- 1873 Volume 6, Number 2 , pp. 215-236,2010. | |
Liyakathunisa, C.N.Ravi Kumar and V.K. Ananthashayana , “Super Resolution Reconstruction of Low Resolution Images using Wavelet Lifting schemes" in Proc ICCEE'09, 2nd International Conference on Electrical Computer Engineering", Dec 28-30TH 2009, Dubai, Indexed in IEEE Xplore. | |
M. Irani and S. Peleg, “Improving resolution by image registration", CVGIP: Graphical Models and Image Proc., vol. 53, pp. 231-239, May 1991. | |
P. Vandewalle, S. Susstrunk, and M. Vetterli, Lcav, “A frequency domain approach to registration of aliased images with application to Super resolution", EURASIP Journal on applied signal processing pp 1-14, 2006. | |
R.Gaughan, “New approaches to early detection of breast cancer makes small gains” ,Biophotonics Int. , pp 48-53, 1998. | |
R.Y. Tsai and T.S. Huang, “Multiple frame image restoration and registration”, in Advances in Computer Vision and Image Processing. Greenwich, CT: AI Press Inc., pp, 317-339. | |
S. C. Park, M. K. Park, and M. G. Kang, “Super-resolution image reconstruction: A technical review”, IEEE Signal Processing Mag., vol. 20, pp. 21-36, May 2003. | |
S. Chaudhuri, Ed., “Super-Resolution Imaging”, Norwell, A: Kluwer, 2001. | |
S. Grace Chang, Bin Yu and M. Vattereli, “Adaptive Wavelet Thresholding for Image denoising and compression", IEEE Transaction, Image Processing, vol. 9, pp. 1532-15460. | |
S. Grace Chang, Bin Yu and M. Vattereli, “Adaptive Wavelet Thresholding for Image denoising and compression”, IEEE Transaction, Image Processing, vol. 9, pp. 1532-15460. | |
S. Shapiro, W. Venet, P.Strax, L.Venet, and R. Roester, ” Ten-to Fourteen year Effect of screening on breast cancer mortality”, JNCL, vol 69, pp 349, 1982. | |
S.K.Mohiden, Perumal,Satik, “Image Denosing using DWT”, IJCSNS, Vol 8, No 1, 2008. | |
S.Susan Young, Ronal G.Diggers, Eddie L.Jacobs, “Signal Processing and performance Analysis for imaging Systems”, ARTEC HOUSE, INC , 2008. | |
Smith R.A., “Epidemiology of breast cancer categorical course in physics”, Tech. Aspects Breast Imaging, Radiol. Sco. N. Amer., pp 21-33, 1993. | |
T. Acharya, P.S. Tsai, “Image up-sampling using Discrete Wavelet Transform", in Proceedings of the 7th International Conference on Computer Vision, Pattern Recognition and Image Processing (CVPRIP). | |
Wang, Bovik, Sheikh, et al, “Image Quality Assessment: From Error Visibility to Structural Similarity”, IEEE Transactions of Image Processing, vol. 13, pp. 1-12, April 2004. | |
Mr. Liyakathunisa
S.J. College of Engineering - India
liyakath@indiatimes.com
Dr. C.N .Ravi Kumar
- India
|
|
|
|
View all special issues >> | |
|
|