AUTOMATIC SEGMENTATION OF BRAIN TUMOR MAGNETIC RESONANCE IMAGING BASED ON MULTI-CONSTRAINS AND DYNAMIC PRIOR

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 2 (June 2015) > List of articles

AUTOMATIC SEGMENTATION OF BRAIN TUMOR MAGNETIC RESONANCE IMAGING BASED ON MULTI-CONSTRAINS AND DYNAMIC PRIOR

Liu Erlin / Wang Meng / Teng Jianfeng / Li Jianjian

Keywords : Brain tumor image, magnetic resonance, segmentation

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 2, Pages 1,031-1,049, DOI: https://doi.org/10.21307/ijssis-2017-794

License : (CC BY-NC-ND 4.0)

Received Date : 01-February-2015 / Accepted: 21-March-2015 / Published Online: 01-June-2015

ARTICLE

ABSTRACT

The most difficult and challenging problem in medical image analysis is image segmentation. Due to the limited imaging capability of magnetic resonance (MR), the sampled magnetic resonance images from clinic always suffer from noise, bias filed (also known as intensity non-uniformity), partial volume effects and motive artifacts. In additional, for the complex shape boundary and topology of brain tissues and structures, segmenting magnetic resonance image of brain tumor fast, accurately and robustly is very difficult. In this paper, we propose an image segmentation algorithm based on multiconstrains and dynamic prior. Through introducing a novel big scale constrain into Markov random filed model from magnetic resonance image we realize automatic segmentation under the principle of maximum a Posterior and a modified expectation-maximization algorithm according to the Bayesian frame. Finally, a set of human body detection and tracking experiments are designed to demonstrate the effectiveness of the proposed algorithms.

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REFERENCES

[1] S. Pradeep, L. Malliga. “Content based image retrieval and segmentation of medical image
database with fuzzy values”, 2014 International Conference on Information Communication
and Embedded Systems, 2014: 1 – 7, doi: 10.1109/ICICES.2014.7034091.
[2] G. Coatrieux, H. Hui, S. Huazhong, L. Limin and C. Roux. “A Watermarking-Based Medical
Image Integrity Control System and an Image Moment Signature for Tampering
Characterization”, IEEE Journal of Biomedical and Health Informatics, 2013, 17(6): 1057 –
1067, doi: 10.1109/JBHI.2013.2263533.
[3] Z. Xiaofan, L. Wei , M. Dundar, S. Badve, Z. Shaoting. “Towards Large-Scale
Histopathological Image Analysis: Hashing-Based Image Retrieval”, IEEE Transactions on
Medical Imaging, 2015, 34(2): 496 – 506: doi: 10.1109/TMI.2014.2361481.
[4] S. Rui Shen, I. Cheng, I and A. Basu. “Cross-Scale Coefficient Selection for Volumetric
Medical Image Fusion”, IEEE Transactions on Biomedical Engineering, 2013, 60(4): 1069 –
1079, doi: 10.1109/ TBME.2012.2211017.
[5] G. Coatrieux, P. Wei. N. Cuppens-Boulahia, F. Cuppens and C. Roux . “Reversible
Watermarking Based on Invariant Image Classification and Dynamic Histogram Shifting”,
IEEE Transactions on Information Forensics and Security, 2013, 8(1): 111 – 120, doi:
10.1109/TIFS.2012.2224108.
[6] H. G. Schnack, P. H. E. Huishoff. “Automatic segmentation of the ventricular system from
MR images of the human brain”, Neurolmage, 2011, 14(1): 95 – 104.
[7] M. Sonka, K. Tadikonda, K. Satish K and M. Collins Steve. “Knowledge-based
interpretation of MR brain images”, IEEE Transactions on Medical Imaging, 1996, 15(4):
443 – 452, doi: 10. 1109/42.511748.
[8] J. Besag. “Spatial interaction and the statistical analysis of lattice systems”, Statistical
Society,Ser B, 1974: 192 –225.
[9] S. M. Smith. “Fast robust automated brain extractio”, Hum.Brain Mapping, 2002, 17(3):
143 – 155.
[10]D. W. Shattuck and S. Sandor-Leahy. “Magnetic resonance image tissue classification using
a partial volume mode”, Neurolmage, 2001, 13(5): 856 – 876.
[11]J. Bezdek. “Pattern Recognition With Fuzzy Objective Function Algorithms”, New York:
Plenum Press, 1981.
[12]Z. Yongyue, M. Brady and S. Smith. “Segmentation of brain MR images through a hidden
Markov random field model and the expectation-maximization algorithm”, IEEE
Transactions on Medical Imaging, 2001, 20(1): 45 – 57: doi: 10.1109/42.906424.
[13]S. Sarkar and K. L. Boyer. “Quantitative measures of change based on feature organization:
eigenvalues and eigenvectors”, 1996 IEEE Computer Society Conference onComputer
Vision and Pattern Recognitio, 1996: 478 – 483, doi: 10.1109/CVPR.1996.517115.
[14]J. Shi, J and J. Malik. “ormalized cuts and image segmentation”, 1997 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition, 1997: 731 – 737, doi:
10.1109/CVPR.1997.609407.
[15]J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha and A. Yuille. “Efficient Multilevel
Brain Tumor Segmentation With Integrated Bayesian Model Classification”, IEEE
Transactions on Medical Imaging, 2008, 27(5): 629 – 640, doi: 10.1109/TMI.2007.912817.
[16]N. Subbanna, D. Precup and T. Arbel. “Iterative Multilevel MRF Leveraging Context and
Voxel Information for Brain Tumour Segmentation in MRI”, IEEE Conference on Computer
Vision and Pattern Recognition, 2014, 400 – 405, doi: 10.1109/CVPR.2014.58.
[17]Shaojun Lu and Chunmin Zhang,adjustment of the parallelism of two mirrors for wide angle
divided mirror michelson wind imaging interferometer, International Journal on Smart
Sensing and Intelligent Systems, vol. 8, no.1, pp. 602 – 619, 2015.
[18]Chastine Fatichah, Diana Purwitasari, et al., overlapping white blood cell segmentation and
counting on microscopic blood cell images, International Journal on Smart Sensing and
Intelligent Systems, vol.7, no.3, pp.1271 – 1286,2014.

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