Brain Image Segmentation Using Conditional Random Field Based On Modified Artificial Bee Colony Optimization Algorithm

Tumor is an uncontrolled growth of tissues in any part
of the body. Tumors are of different types and they have different
characteristics and treatments. Brain tumor is inherently serious and
life-threatening because of its character in the limited space of the
intracranial cavity (space formed inside the skull). Locating the tumor
within MR (magnetic resonance) image of brain is integral part of the
treatment of brain tumor. This segmentation task requires
classification of each voxel as either tumor or non-tumor, based on
the description of the voxel under consideration. Many studies are
going on in the medical field using Markov Random Fields (MRF) in
segmentation of MR images. Even though the segmentation process
is better, computing the probability and estimation of parameters is
difficult. In order to overcome the aforementioned issues, Conditional
Random Field (CRF) is used in this paper for segmentation, along
with the modified artificial bee colony optimization and modified
fuzzy possibility c-means (MFPCM) algorithm. This work is mainly
focused to reduce the computational complexities, which are found in
existing methods and aimed at getting higher accuracy. The
efficiency of this work is evaluated using the parameters such as
region non-uniformity, correlation and computation time. The
experimental results are compared with the existing methods such as
MRF with improved Genetic Algorithm (GA) and MRF-Artificial
Bee Colony (MRF-ABC) algorithm.





References:
[1] Bouchet A, Pastore J and Ballarin V, “Segmentation of Medical Images
using Fuzzy Mathematical Morphology”, JCS and T, Vol.7, No.3,
pp.256-262, October 2007.
[2] N. Senthilkumaran and R. Rajesh, “Edge Detection Techniques for
Image Segmentation-A Survey of Soft Computing Approaches”,
International Journal of Recent Trends inEngineering, Vol.1, No.2,
pp.250-254, May 2009.
[3] Dao QiangZhanga and Song Can Chena, “A novel kernelized fuzzy Cmeans
algorithm with application in medical image segmentation”,
Artificial Intelligence in Medicine, Vol. 32, pp.37-50, 2004.
[4] Ian Middleton and Robert I. Damper, “Segmentation of magnetic
resonance images using a combination of neural networks and active
contour models”, Medical Engineering and Physics, Vol.26, pp.71-86,
2004.
[5] M. E. Brummer, “Optimized intensity thresholds for volumetric analysis
of magnetic resonance imaging data”, Proc. SPIE, Vol.1808, pp. 299-
310, 1992.
[6] A. Kundu, “Local segmentation of biomedical images,” Comput. Med.
Imag. Graph., Vol. 14, pp. 173-183, 1990.
[7] A.M. Bishop, “Neural Networks for Pattern Recognition”, Oxford, UK:
Oxford Univ., 1995.
[8] W. M.Wells, E. L. Grimson, R. Kikinis, and F. A. Jolesz, “Adaptive
segmentation of MRI data”, IEEE Trans. Med. Imag., Vol.15, pp. 429–
442, Aug. 1996.
[9] R. Guillemaud and J. M. Brady, “Estimating the bias field of MR
images”, IEEE Trans. Med. Imag., Vol.16, pp. 238-251, June 1997.
[10] C. Li, D. B. Godlgof, and L. O. Hall, “Knowledge-based classification
and tissue labeling of MR images of human brain”, IEEE Trans.
Med.Imag., Vol.12, pp. 740-750, Dec.1993.
[11] M. E. Brummer, R. M. Mersereau, R. L. Eisner and R. R. J. Lewine,
“Automatic detection of brain contours in MRI data sets”, IEEE
Trans.Med. Imag., Vol. 12, pp. 153-166, June 1993.
[12] H. A. Rowley, S. Baluja, and T. Kanade, “Neural Network Based Face
Detection”, IEEE Transactions on Pattern Analysis and Machine
Intelligence, pp. 23-38, Jan.1998.
[13] S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions,
and the Bayesian restoration of images”, IEEE Trans. Pattern Anal.
Machine Intell., No. PAMI-6, pp. 721-741, June 1984.
[14] J. Besag, “On the statistical analysis of dirty pictures (with discussion)”,
J. of Royal Statist. Soc., Ser. B, Vol.48, No.3, pp. 259-302, 1986.
[15] S. Z. Li, “Markov Random Field Modeling in Computer Vision”. Berlin,
Germany: Springer-Verlag, 1995.
[16] K. Held, E. R. Kops, B. J. Krause,W. M.Wells, and R. Kikinis, “Markov
random field segmentation of brain MR images”, IEEE Trans. Med.
Imag., Vol.16, pp.878-886, Dec. 1997.
[17] Stefan Bauer1,3, Roland Wiest2, Lutz-P Nolte1 and Mauricio Reyes, “A
survey of MRI-based medical image analysis for brain tumor studies”,
Phys. Med. Biol. 58 (2013) R97–R129.
[18] Resmi A*,1, Thomas T, Thomas B, “A novel automatic method for
extraction of gliomatumour, white matter and grey matter from brain
magnetic resonance images”, Biomedical Imaging and Intervention
Journal, J 2013; 9(2):e21.
[19] Nelly Gordillo, Eduard Montseny, PilarSobrevilla, “State of the art
survey on MRI brain tumor segmentation,” Elisevier. (2013) 1426–1438.
[20] Wedad S. Salem, Ahmed F. Seddik, Hesham F. Ali1, “A Review on
Brain MRI Image Segmentation”, Computers and Systems Department,
Electronics Research Institute, Cairo, Egypt.
[21] B. Basturk, DervisKaraboga, “An Artificial Bee Colony (ABC)
Algorithm for Numeric function Optimization”, IEEE Swarm
Intelligence Symposium, May, 2006.
[22] E. Ben George, M.Karnan, “MR Brain Image Segmentation using
Bacteria Foraging Optimization Algorithm”, International Journal of
Engineering and Technology (IJET), Vol.4, No.5, Oct.-Nov. 2012.
[23] Lafferty, J., McCallum, A., & Pereira, F, “Conditional random fields:
Probabilistic models for segmenting and labeling sequence data”, Proc.
18th International Conf. on MachineLearning, 2001.
[24] DusanT,TatjanaD,Milica S “Bee Colony Optimization Overview”, BCO
chapter.
[25] Cuevas, E., Sención-Echauri, F., Zaldivar, D., Pérez-Cisneros, M. Multicircle
detection on images using artificial bee colony (ABC)
optimization, Soft Computing, 16 (2), (2012), pp. 281-296.