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A Comparison of X-Ray Image Segmentation TechniquesSTOLOJESCU-CRISAN, C. , HOLBAN, S.
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image processing, image segmentation, biomedical imaging, digital images, X-rays
segmentation(30), image(30), images(11), medical(10), processing(9), analysis(7), techniques(6), automatic(6), active(6), technology(5)
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About this article
Date of Publication: 2013-08-31
Volume 13, Issue 3, Year 2013, On page(s): 85 - 92
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.03014
Web of Science Accession Number: 000326321600014
SCOPUS ID: 84884928131
Image segmentation operation has a great importance in most medical imaging applications, by extracting anatomical structures from medical images. There are many image segmentation techniques available in the literature, each of them having advantages and disadvantages. The extraction of bone contours from X-ray images has received a considerable amount of attention in the literature recently, because they represent a vital step in the computer analysis of this kind of images. The aim of X-ray segmentation is to subdivide the image in various portions, so that it can help doctors during the study of the bone structure, for the detection of fractures in bones, or for planning the treatment before surgery. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. We will discuss the principle and the mathematical model for each method, highlighting the strengths and weaknesses.
|References|||||Cited By «-- Click to see who has cited this paper|
| V. Zharkova, S. Ipson, J. Aboudarham and B. Bentley, "Survey of image processing techniques", EGSO internal deliverable, Report number EGSO-5-D1_F03-20021029, October, 2002, 35p. [Online] Available: Temporary on-line reference link removed - see the PDF document
 D. Feng, "Segmentation of Bone Structures in X-ray Image", PhD thesis, School of Computing National University of Singapore, under guidance of Dr. Leow Wee Kheng (Associate Professor), 2006. [Online] Available: Temporary on-line reference link removed - see the PDF document
 G. Dougherty. Medical Image Processing Techniques and Applications. Springer, 2011.
[CrossRef] [Web of Science Times Cited 32]
 J. C. Russ. Image Processing Handbook, the Sixth Edition. CRC Press Taylor & Francis Group, 2011.
 I. N. Bankman. Handbook of Medical Imaging Processing and Analysis. Academic Press, 2000.
 J. L. Prince, D. L.Pham, and C. Xu, "A survey of current methods in medical image segmentation ", in Annual Review of Biomedical Engineering, 2:315-338, 2000.
[CrossRef] [Web of Science Times Cited 1266] [SCOPUS Times Cited 1533]
 T. S. Yoo. Insight Into Images Principles and Practice for Segmentation, Registration, and Image Analysis. A K Peters Wellesley, Massachusetts, 2004.
 S. V. Kasmir Raja, A. Shaik Abdul Khadir, and S. S. Riaz Ahamed, "Moving toward region-based image segmentation techniques: a study", Journal of Theoretical and Applied Information Technology, 5:81-87, 2009.
 W. Burgern and M. J. Burge. Principles of Digital Image Processing Fundamental Techniques. Springer, 2009.
 J. Bozek, M. Mustra, K. Delac, and M. Grgic, "A survey of image processing algorithms in digital mammography ", Advances in Multimedia Signal Processing and Communications, pp. 631-657, 2009.
[CrossRef] [SCOPUS Times Cited 69]
 P. Meer and D. Comaniciu, "Mean shift: A robust approach toward feature space analysis ", IEEE Trans. Pattern Analysis Machine Intell, 24(5), pp.603-619, 2002.
[CrossRef] [Web of Science Times Cited 6593] [SCOPUS Times Cited 8835]
 B. R. Abidi, J. Liang and M. A. Abidi,"Automatic x-ray image segmentation for threat detection ", Proc. of the Fifth International Conference on Computational Intelligence and Multimedia Applications, 2003.
 M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active Contours ", International Journal of computer Vision, pp.321-331, 1988.
[CrossRef] [Web of Science Times Cited 9454] [SCOPUS Times Cited 12309]
 M. Kulkarni, "X-ray image segmentation using active shape models", Master's thesis, University of Cape Town, 2008. [Online] Available: Temporary on-line reference link removed - see the PDF document
 H. Mosto, "Fast level set segmentation of biomedical images using graphics processing units", Technical report, Keble College, 2009. [Online] Available: Temporary on-line reference link removed - see the PDF document
 B. N. Li, C. K. Chui, S. Chang, and S. H. Ong, "Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation", Elsevier - Computers in Biology and Medicine, no.10, pp.1-10, 011.
[CrossRef] [Web of Science Times Cited 258] [SCOPUS Times Cited 330]
 O. Matei, "Ontology-based knowledge organization for the radiograph images segmentation ", Advances in Electrical and Computer Engineering, 8 (15), pp.56-61, 2008.
[CrossRef] [Full Text] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]
 M. S. Brown , L. S. Wilson, B. D. Doust, R.W. Gill, and C. Sun , "Knowledge-based method for segmentation and analysis of lung boundaries in chest X-ray images ", Elsevier Computerized Medical Imaging and Graphics, no.2, pp.463-477, 1998.
[CrossRef] [Web of Science Times Cited 69] [SCOPUS Times Cited 79]
 D. Davis, S. Linying, and B. Sharp, "Neural Networks for X-Ray Image Segmentation ", Proc.of the First International Conference on Enterprise Information System, pp. 264-271, 1999.
 H. K. Huang, M. F. McNitt-Gray and J. W. Sayre, "Feature selection in the pattern classification problem of digital chest radiograph segmentation ", IEEE Transactions on Medical Imaging, no. 14(3), pp.537-547, 1995.
[CrossRef] [Web of Science Times Cited 100] [SCOPUS Times Cited 113]
 S. Linying, B. Sharp, and C. C. Chibelushi,"Knowledge-Based Image Understanding: A Rule-Based Production System for X-Ray Segmentation", Proc. of the 4th International Conference on Enterprise Information Systems, vol. 1, pp. 530 - 533, Spain, 2002.
 I. El-Feghi, "X-ray image segmentation using auto adaptive fuzzy index measure", Proc. of the 47th Midwest Symposium on Circuits and Systems, vol.3, pp. 499-502, 2004.
 A. A. Tirodkar, "A Multi-Stage Algorithm for Enhanced XRay Image Segmentation", International Journal of Engineering Science and Technology (IJEST), Vol. 3 No. 9, pp. 7056-7065, 2011.
 S. K. Mahendran and S. S. Baboo, "Enhanced automatic X-ray bone image segmentation using wavelets and morphological operators", Proc. of the International Conference on Information and Electronics Engineering, 2011.
 G. K. Manos, A. Y. Cairn, I. W. Rickets and D. Sinclair, "Segmenting radiographs of the hand and wrist", Elsevier Computer Methods and Programs in Biomedicine, vol. 43 (3-4), pp.227-237, 1993.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 32]
 P. Annangi, S. Thiruvenkadam, A. Raja, H. Xu, X. W. Sun, and L. Mao "A region based active contour method for X-ray lung segmentation using prior shape and low level features", Proc. of the International Symposium on Biomedical Imaging, pp. 892- 895, 2010.
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 68]
 E. H. Said, G. Fahmy, D. Nassar, and H.Ammar, "Dental X-ray Image Segmentation", Proc. of the SPIE, vol. 5404, pp. 409-417, 2004.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 28]
 Y. Chen, X. Ee, W. K. Leow and T. S. Howe, Automatic extraction of femur contours from hip X-ray images ", Proc. of the First International Workshop on Computer Vision for Biomedical Image Applications, 3765, pp. 200-209, 2005.
[CrossRef] [SCOPUS Times Cited 42]
 C. Ying, "Model-based approach for extracting femur contours in x-ray images", Master's thesis, National University of Singapore, 2005.
 M. Seise, S. J. McKenna, I. W. Ricketts and C. A. Wigderowitz, "Segmenting tibia and femur from knee X-ray images", Proc. of Medical Image Understanding and Analysis, pp. 103- 106, 2005.
 H. Chen and A. K. Jain, "Tooth contour extraction for matching dental radiographs" Proc. International Conference on Pattern Recognition, pp. 522-525, 2004.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 45]
 L. Ballerini, and L.Bocchi, "Bone segmentation using multiple communicating snakes", Proc. of the International Symposium Medical Imaging, 2003.
 C. J. Taylor, T. F. Cootes and A. Lanitis, "Active shape models: Evaluation of a multi-resolution method for improving image search ", Proc. of the 5th British Machine Vision Conference, pp. 327-336, 1994.
 G. Zamora-Camarena "Automatic segmentation of vertebrae from digitized X-ray images",PhD thesis, Texas Tech University, 2002. [Online] Available: Temporary on-line reference link removed - see the PDF document
 G. Behiels, D. Vandermeulen, F. Maes, P. Suetens, and P. Dewaele, "Active shape model-based segmentation of digital X-ray images ", Proc. of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 128-137, 1999.
[CrossRef] [SCOPUS Times Cited 53]
 N. Boukala, "Active shape model based segmentation of bone structures in hip radiographs ", Proc. of the International Conference on Industrial Technology, pp. 1682-1687, 2004.
 F. Ding, W. K. Leow, and T. S. Howe, "Automatic Segmentation of Femur Bones in Anterior-Posterior Pelvis X-Ray Images", Proc. of the 12th International Conference on Computer Analysis of Images and Patterns, 2007, pp. 205-212.
[CrossRef] [SCOPUS Times Cited 20]
 N. Senthilkumaran and R. Rajesh, "Edge detection techniques for image segmentation - a survey of soft computing approaches ", International Journal of Recent Trends in Engineering, 1, pp. 250-255, 2009.
 M. A. Ali, L. S. Dooley and G. C. Karmakar, "Object Based Image Segmentation Using Fuzzy Clustering ", Proc. of International Conference on Acoustics, Speech, and Signal Processing, 2006, pp. 105-108.
 L. Florea, C. Florea, C. Vertan and A. Sultana, "Automatic Tools for Diagnosis Support of Total Hip Replacement Follow-up ", Advances in Electrical and Computer Engineering, vol.11, no.4, pp.55- 63, 2011.
[CrossRef] [Full Text] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]
 S. Binitha, S Siva Sathya, "A Survey of Bio inspired Optimization Algorithms", International Journal of Soft Computing and Engineering (IJSCE), ISSN: 2231-2307, vol.2, Issue 2, May 2012.
 X. Wang, B.S. Wong, C. G. Tui, "X-ray image segmentation based on genetic algorithm and maximum fuzzy entropy", Proc. IEEE Conference on Robotics, Automation and Mechatronics, pp.991-995, 2004.
 N. Senthilkumaran, "Genetic Algorithm Approach to Edge Detection for Dental X-ray Image Segmentation", International Journal of Advanced Research in computer Science and Electronics Engeneering, vol.1, no.7, 2012.
 E. Bonabeau, M. Dorigo and G. Theraulaz. Swarm intelligence. Oxford University Press, 1999.
 F. Keshtkar, "Segmentation of Dental Radiographs Using a Swarm Intelligence Approach", Proc. of Canadian Conference on Electrical and Computer Engineering, 2006, pp. 328- 331.
[CrossRef] [SCOPUS Times Cited 23]
 T. Sag, M. Cunkas, "Development of Image Segmantation Techniques Using Swarm Intelligence", Proc. of the 1st Taibah University International Conference on Computing and Information Technology, pp.95-100, 2011.
 A. V. Alvarenga, "Artificial Ant Colony: Features and applications on medical image segmentation", Pan American Health Care Exchanges Conference, pp. 96-101, 2011.
[CrossRef] [SCOPUS Times Cited 4]
 J. Rahebi , H. R. Tajik , "Biomedical Image Edge Detection using an Ant Colony Optimization Based on Artificial Neural Networks", International Journal of Engineering Science and Technology, 2011.
 S. Gupta, G. S. Sandhu and N. Mohan, "Implementing Color Image Segmentation Using Biogeography Based Optimization", Proc. of the International Conference on Computer and Communication Technologies, pp. 167-170, 2012.
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