We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

Knee Joint Articular Cartilage Segmentation, Visualization and Quantification using Image Processing Techniques: A Review

by M. S. Mallikarjuna Swamy, Mallikarjun S. Holi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 19
Year of Publication: 2012
Authors: M. S. Mallikarjuna Swamy, Mallikarjun S. Holi
10.5120/5804-8151

M. S. Mallikarjuna Swamy, Mallikarjun S. Holi . Knee Joint Articular Cartilage Segmentation, Visualization and Quantification using Image Processing Techniques: A Review. International Journal of Computer Applications. 42, 19 ( March 2012), 36-43. DOI=10.5120/5804-8151

@article{ 10.5120/5804-8151,
author = { M. S. Mallikarjuna Swamy, Mallikarjun S. Holi },
title = { Knee Joint Articular Cartilage Segmentation, Visualization and Quantification using Image Processing Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 19 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 36-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number19/5804-8151/ },
doi = { 10.5120/5804-8151 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:46.264116+05:30
%A M. S. Mallikarjuna Swamy
%A Mallikarjun S. Holi
%T Knee Joint Articular Cartilage Segmentation, Visualization and Quantification using Image Processing Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 19
%P 36-43
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Knee is a complex and articulated joint of the body. Cartilage is a smooth hyaline spongy material between the tibia and femur bones of knee joint. Cartilage morphology change is an important biomarker for the progression of osteoarthritis (OA). Magnetic resonance imaging (MRI) is the modality widely used to image the knee joint because of its hazard free and high resolution soft tissue contrast. Cartilage thickness measurement and visualization is useful for early detection and progression of the disease in case of OA affected patients. A wide variety of algorithms are available for knee joint image segmentation. They are classified as pixel based and model based methods. Based on the human intervention required, segmentation methods are also classified as manual, semi-automatic and fully automatic methods. This paper reviews knee joint articular cartilage segmentation methods, visualization, thickness measurement, volume measurement and validation methods.

References
  1. Samson DJ, Grant MD, Ratko TA, Bonnell CJ, Ziegler KM and Aronson N. "Treatment of primary and secondary osteoarthritis of the knee", Evidence Report, AHRQ Publication, Technology Assessment No. 157, No. 07-E012. September 2007.
  2. Sharma MK, Swami HM, Bhatia V, Verma A, Bhatia S, and Kaur G. , "An epidemiological study of correlates of osteo-arthritis in geriatric population of UT Chandigarh", Indian Journal of Community Medicine, vol 32, pp. 77-8, 2007.
  3. Dzung L. Pham, Chenyang Xu, and Jerry L. Prince, "Current methods in medical image segmentation", Annual Review of Biomedical Engineering, vol. 02, pp. 315–37, 2000
  4. Kshirsagar, M. D. Robson, P. J. Watson, N. J. Herrod, J. A. Tyler and L. D. Hall, "Computer analysis of MR images of human knee joints to measure femoral cartilage thickness", Proc. of 18th Annual Int. Conf. IEEE Engineering in Medicine and Biology, Amsterdam, 1996, pp. 746-747.
  5. Kshirsagar, P. J. Watson, N. J. Herrod, J. A. Tyler, and L. D. Hall, "Quantification of articular cartilage dimensions by computer analysis of 3D MR images of human knee joints", Proc. 19th Int. Conf. IEEE EMBS, Chicago, IL. USA, 1997, pp. 753-756
  6. John Canny, "A Computational Approach to Edge Detection", IEEE Trans. On Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp 679-698, 1986.
  7. Zohara A. Cohen, Denise M. Mccarthy, S. Daniel Kwak, Perrine Legrand, Fabian Fogarasi, Edward J. Ciaccio And Gerard A. Ateshian, "Knee cartilage topography, thickness, and contact areas from MRI: in-vitro calibration and in-vivo measurements", Osteoarthritis and Cartilage, vol. 7, pp. 95–109, 1999.
  8. Póth Miklós, "Comparison of convolutional based interpolation techniques in digital image processing", Proc. of 5th International Symposium on Intelligent Systems and Informatics, July 24-25, 2007, Subotica, Serbia, pp 87-90.
  9. Peter M. M. Cashman, Richard I. Kitney, Munir A. Gariba, and Mary E. Carter, "Automated techniques for visualization and mapping of articular cartilage in MR images of the osteoarthritic knee: a base technique for the assessment of microdamage and submicro damage", IEEE Trans. on Nanobioscience, vol. 1, no. 1, pp. 42-51, 2002.
  10. C. L. Poh and R. I. Kitney, "Viewing interfaces for segmentation and measurement results", Proc. 27th Annual Conf. IEEE Engineering in Medicine and Biology, Shanghai, China, 2005, pp. 5132-5135.
  11. Julio Carballido-Gamio, Jan S. Bauer1, Keh-Yang Lee, Stefanie Krause, and Sharmila Majumdar, "Combined image processing techniques for characterization of MRI cartilage of the knee", Proc. 27th Annual Conf. IEEE Engineering in Medicine and Biology, Shanghai, China, 2005 , pp. 3043-3046.
  12. Hackjoon Shim, Samuel Chang,Cheng Tao,Jin-Hong Wang,C. Kent Kwoh, and Kyongtae T. Bae, "Knee cartilage: efficient and reproducible segmentation on high- spatial-resolution MR images with the semiautomated graph-cut algorithm method1", Radiology, vol. 251, no. 2, pp. 548-556, 2009.
  13. J. Carballido-Gamio, K. Lee1, E. Ozhinsky, S. Majumdar, "MRI cartilage of the knee: segmentation, analysis, and visualization", Proc. Intl. Soc. Mag. Reson. Med. 2004, p-210.
  14. Jiann-Shu Lee and yi-Nung Chung, "Integrating edge detection and thresholding approaches to segmenting femora and patellae from magnetic resonance images", Biomedical Engineering Applications, Basis & Communications, vol. 17, no. 1, pp. 1-11, 2005.
  15. Pierre Dodin, Jean-Pierre Pelletier, Johanne Martel-Pelletier, and François Abram, "Automatic human knee cartilage segmentation from 3D magnetic resonance images", IEEE Trans. on Biomedical Engineering, vol. 57, no. 11, pp. 2699-2711, 2010.
  16. Ku-Yaw Chang, Shao-Jer Chen, Lih-Shyang Chen, and Cheng-Jung Wu, "Articular cartilage segmentation based on radial transformation", Proc. of 9th International Conf. on Hybrid Intelligent Systems 2009, pp. 239-242.
  17. Srinka Ghosh, Olivier BeufL, Michael Ries , Nancy E. Lane, Lynne S Steinbach, Thomas M. Link, and Sharmila Majumdar, "Watershed segmentation of high resolution magnetic resonance images of articular cartilage of the knee", Proc. of the 22nd Int. Conf. Annual EMBS, 2000, pp. 3174-3176.
  18. V. Grau, A. U. J. Mewes, M. Alcañiz, R. Kikinis, and S. K. Warfield, "Improved watershed transform for medical image segmentation using prior information", IEEE Trans. on Medical Imaging, vol. 23, no. 4, pp. 447-458, 2004.
  19. Jenny Folkesson, Erik B. Dam, Ole F. Olsen, Paola C. Pettersen, and Claus Christiansen, "Segmenting articular cartilage automatically using a voxel classification approach", IEEE Trans. on Medical Imaging, vol. 26, no. 1, pp. 106-115, 2007.
  20. Cristián Tejos, Laurance D. Hall, and Arturo Cárdenas-Blanco, "Segmentation of articular cartilage using active contours and prior knowledge", Proc. of the 26th Annual Int. Conf. of the IEEE EMBS, San Francisco, CA, USA , 2004, pp. 1648-1651.
  21. Sonka, Hlavac and Boyle, "Digital image processing and computer vision", Cenage Learning, 2008.
  22. Thi-Thao Tran, Po-Lei Lee, Van-Truong Pham and Kuo-Kai Shyu, "MRI Image segmentation based on fast global minimization of snake model", Proc. of 10th Intl. Conf. on Control, Automation, Robotics and Vision, Hanoi, Vietnam, 2008, pp. 1769-1772.
  23. Claude Kauffmann, Pierre Gravel, Benoît Godbout, Alain Gravel, Gilles Beaudoin, Jean-Pierre Raynauld, Johanne Martel-Pelletier, Jean-Pierre Pelletier, and Jacques A. de Guise, "Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model", IEEE Trans. on Biomedical Engineering, vol. 50, no. 8, pp. 978-988, 2003.
  24. Tina Kapur, Paul A. Beardsley, Sarah F. Gibson, W. Eric L. Grimson, and William M. Wells, "Model based segmentation of clinical knee MRI", Proc. of the 6th Int. Conf. on Computer Vision (ICCV-98), Bombay, India, 1998.
  25. Jinshan Tang, Steven Millington, Scott T. Acton, Jeff Crandall, and Shepard Hurwitz, "Surface extraction and thickness measurement of the articular cartilage from MR images using directional gradient vector flow snakes", IEEE Trans. on Biomedical Engineering, vol. 53, no. 5, pp. 896-907, 2006.
  26. Hussain Z. Tameem, Luis E. Selva, and Usha S. Sinha, "Morphological atlases of knee cartilage: shape indices to analyze cartilage degradation in osteoarthritic and non-osteoarthritic population", Proc. of 29th Annual Int. Conf. of the IEEE EMBS, Cité Internationale, Lyon, France, 2007, pp. 1310-1313.
  27. Jurgen Fripp, Sebastien Ourselin, Simon K. Warfield, and Stuart Crozier, "Automatic segmentation of the bones from MR images of the knee", Proc. IEEE 4th Int. Symposium on Biomedical Imaging (ISBI-'07), Metro Washington DC, USA, 2007, pp. 336-339.
Index Terms

Computer Science
Information Sciences

Keywords

Cartilage Thickness Mri Osteoarthritis Knee Joint Image Segmentation