Implementation of Hybrid Error Model Using Super Resolution for Medical Images
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - August Issue 2013 by IJCTT Journal|
|Volume-4 Issue-8 |
|Year of Publication : 2013|
|Authors :Navdeep Kaur, Usvir Kaur|
Navdeep Kaur, Usvir Kaur "Implementation of Hybrid Error Model Using Super Resolution for Medical Images "International Journal of Computer Trends and Technology (IJCTT),V4(8):2417-2422 August Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract:- In pattern recognition, for various domains different models or combination of models can be used. In case of noisy patterns, choice of statistical model is a good solution. Practical importance of structural model depends upon recognition of simple pattern primitives and their relationships represented by description language. Hybrid model is combination of both statistical and structure model. So it is best method to solve the many problems. In hybrid model, we use hybrid error model for super resolution of images Conventional X-ray imaging is the fastest, most common, and least expensive diagnostic imaging system available. The aim of this paper is to present a model using super resolution for removing the noise in digital X-ray images .The resulting X-ray images are more visible ,noise is reduced from X-ray images. We implement the model using frequency domain instead maximum likelihood because it gives better results in medical images. With the help of super resolution, we increase the resolution of the image that also increases the detail of the image. When we remove the noise from the image, image quality also increases that helps us to find the clearly symptom of any diseases.
 Introduction to pattern recognition; Web source by Wikipedia.
 M.Subba Rao, Dr.B.Eswara Reddy, “Comparative Analysis of Pattern Recognition Methods: An Overview”, Indian Journal of Computer Science and Engineering (IJCSE).
 Anil K. Jain, Robert P. W. Duin, and Jianchang Mao, “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1):4 –37, January 2000.
 Mayank Parasher, Shruti Sharma, A.K Sharma. “Anatomy On Pattern Recognition”, Indian Journal Of Computer Science And Engineering (IJCSE).
 Sameer Antani, Rangachar Kasturi, Ramesh Jain, “A survey on the use of pattern recognition methods for abstraction, indexing and retrieval of images and video”,the journal of the pattern recognition society,2001.
 Sina Farsiu, M. Dirk Robinson, Michael Elad, And Peyman Milanfar, “Fast And Robust Multiframe Super Resolution”,IEEE Transactions On Image Processing, Vol. 13, No. 10, October 2004.
 Huihui Song , Lei Zhang, Peikang Wang , Kaihua Zhang And Xin Li, “An Adaptive L1-L2 Hybrid Error Model To Super-Resolution”, Image Processing (ICIP), 2010 17th IEEE International Conference.
 Jie Liu,Jigui Sun,Shengsheng Wang, “Pattern Recognition: An overview”, IJCSNS International Journal of Computer Science and Network Security, VOL.6 No.6, June 2006.
 T.Pavlidis, “Structural Pattern Recognition,” New York: Springer-Verlag,2007
 Lihong Zheng and Xiangjian He, “Classification Techniques in Pattern Recognition”, IEEE Transactions on, Volume:25, Issue: 10 , pp:1253 – 1264,Oct. 2007.
 S.Park, M.Park, and M.Kang, “Supper-Resolution Image Reconstruction: A Technical Overview”, IEEE Signal Processing Magazine, vol.20, pp.21–36, 2007.
 Hayit Greenspan, “Super-Resolution In Medical Imaging”, Advance Access Publication On February 19, 2008.
 Tasweer Ahmad.Ahlam Jameel,Dr. Balal Ahmad, "Pattern Recognition using Statistical Techniques", IEEE 2011.
. C. Lee and D.A. Landgrebe, “Feature Extraction Based on Decision Boundaries,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 4, pp. 388-400, 2011.
 Seema Asht ,Rajeshwar Dass, “Pattern Recognition Techniques: A Review”, International Journal of Computer Science and Telecommunications [Volume 3, Issue 8, August 2012].
Keywords : — Super-resolution, Frequency domain, X-ray imaging, Gaussian and Laplacian distribution .