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Segmentation of CT image using MAP-Model and Simulation Annealing

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 2
Year of Publication: 2013
Md. Gauhar Arefin
Mithun Kumar P K.
Abu Sayem
Md Delowar Hossain
Md Motiur Rahman

Md. Gauhar Arefin, Mithun Kumar P K., Abu Sayem, Md Delowar Hossain and Md Motiur Rahman. Article: Segmentation of CT image using MAP-Model and Simulation Annealing. International Journal of Computer Applications 61(2):45-48, January 2013. Full text available. BibTeX

	author = {Md. Gauhar Arefin and Mithun Kumar P K. and Abu Sayem and Md Delowar Hossain and Md Motiur Rahman},
	title = {Article: Segmentation of CT image using MAP-Model and Simulation Annealing},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {61},
	number = {2},
	pages = {45-48},
	month = {January},
	note = {Full text available}


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.


  • 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.
  • 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.
  • S. Kirkpatrick, C. D. Gelatt Jr, and M. P. Vecchi, "Optimization by simulated annealing," Science, vol. 220, no. 4598, pp. 671-680, 1983.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • J. M. Hammersley, and P. Clifford, "Markov field on finite graphs and lattices", unpublished, 1971.
  • 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.
  • 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.