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Segmentation of CT image using MAP-Model and Simulation Annealing

by Md. Gauhar Arefin, Mithun Kumar P K., Abu Sayem, Md Delowar Hossain, Md Motiur Rahman
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 2
Year of Publication: 2013
Authors: Md. Gauhar Arefin, Mithun Kumar P K., Abu Sayem, Md Delowar Hossain, Md Motiur Rahman
10.5120/9904-4492

Md. Gauhar Arefin, Mithun Kumar P K., Abu Sayem, Md Delowar Hossain, Md Motiur Rahman . Segmentation of CT image using MAP-Model and Simulation Annealing. International Journal of Computer Applications. 61, 2 ( January 2013), 45-48. DOI=10.5120/9904-4492

@article{ 10.5120/9904-4492,
author = { Md. Gauhar Arefin, Mithun Kumar P K., Abu Sayem, Md Delowar Hossain, Md Motiur Rahman },
title = { Segmentation of CT image using MAP-Model and Simulation Annealing },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 2 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 45-48 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number2/9904-4492/ },
doi = { 10.5120/9904-4492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:02.987798+05:30
%A Md. Gauhar Arefin
%A Mithun Kumar P K.
%A Abu Sayem
%A Md Delowar Hossain
%A Md Motiur Rahman
%T Segmentation of CT image using MAP-Model and Simulation Annealing
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 2
%P 45-48
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation on Computed Tomography (CT) image of heart and brain can be optimally posed as Bayesian labeling in which the segment of a image is defined as the maximum a posteriori (MAP) probability estimate of the true labeling. The Simulated Annealing (SA) algorithm is used to minimize the energy function associated with MRF posterior distribution function. The goal of this thesis paper is to minimize the energy function using Gaussian distribution and get accurate segmentation by slowly minimize the energy and simultaneously reduce the pixels which have no impact on the image at rapid rate to get the segmentation quickly without degrade the image. The propose algorithm able to get more precise segmentation.

References
  1. L. Lucchese, S. K. Mitra, "Color Image Segmentation: A State of the Art Survey," (invited paper), Image Processing, Vision and Pattern Recognition, Proc. of Indian National Science Academy, vol. 67, A, No. 2, pp. 207-221, March-2001.
  2. H. D. Cheng, X. H. Jiang, Y. Sun and Jingli Wang, "Color Image Segmentation: Advances and Prospects," Pattern Recognition, vol. 34, pp. 2259-2281, 2001.
  3. S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983.
  4. H. Deng, and D. A. Clausi, "Unsupervised image segmentation using a simple MRF model with a new implementation scheme," Pattern Recognit. , vol. 37, no. 12, pp. 2323-2335, 2004.
  5. S. Geman, D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images", IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 6, pp. 721-741, 1984.
  6. Shen S, Szameitat AJ, Sterr A. "An improved lesion detection approach based on similarity measurement between fuzzy intensity segmentation and spatial probability maps. Magnetic Resonance Imaging ". 2010; 28(2):245-54.
  7. Yong Y, Pan L, Chongxun Z, editors "An efficient statistical method for segmentation of single channel brain MRI Computer and Information Technology", 2004. CIT '04. The Fourth International Conference on; 2004 14-16 Sept. 2004.
  8. Xia Y, Wen L, Eberl S, Fulham M, Feng D. "Segmentation of dual modality brain PET/CT images using the MAP-MRF model". Multimedia Signal Processing, 2008 IEEE 10th Workshop on, IEEE. 2008:107–110.
  9. Y. Xia, L. Wen, S. Eberl, M. Fulham, and D. Feng, "Segmentation of brain structures using PET-CT images," in Proc. of the 5th International Conference on Information Technology and Applications in Biomedicine (ITAB 2008), Shenzhen, China, May 30-31, 2008.
  10. Geman S, Geman D, Abend K, Harley T, Kanal L. "Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images". Journal of Applied Statistics. 1993; 20(5):25-62.
  11. Stolkin R, Greig A, Hodgetts M, Gilby J. "An EM/E-MRF algorithm for adaptive model based tracking in extremely poor visibility". Image and Vision Computing. 2008;26(4):480-95.
  12. S. Z. Li, "Modeling image analysis problems using Markov random fields," in C. R. Rao and D. N. Shanbhag (ed), Stochastic Processes: Modeling and simulation, vol. 20 of Handbook of Statistics. Elsevier Science, 2000, pp. 1-43.
  13. J. M. Hammersley, and P. Clifford, "Markov field on finite graphs and lattices", unpublished, 1971.
  14. Sucheta Panda and P. K. Nanda, "Unsupervised Color Image Segmentation Using Compound Markov Random Field Model" Proc. of International Conference on Pattern Recognition and Machine Intelligence (PReMI-09), 16-20 Dec, 2009, IIT Delhi, India.
  15. Y. Xia, D. Feng, and R. Zhao, "Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model," IEEE Trans. Image Processing, vol. 15, pp. 3559–3566, Nov. 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Computer Tomography (CT) Maximum a Posteriori probability (MAP) Markov Random Field (MRF) Simulated Annealing (SA) algorithm

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