View More View Less
  • 1 Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
  • 2 Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
  • 3 National Defence University of Malaysia, Kuala Lumpur, Malaysia

Segmentation is one of important methods in medical images processing, particularly as it allows images to be analysed. The method used for segmentation depends on the image problem to be resolved. In this research, knee cartilage needs to be segmented to determine the level of the Osteoarthritis (OA) and for further treatment. Knee cartilage is a soft hyline sponge that is located at the end of the femur, tibia and patella bone to release friction during movement. OA is a knee cartilage problem wherein there is a thinning of the cartilage that results in a shift especially happening between femur and tibia bone causing discomfort and pain. Thinning of the knee cartilage is due to many factors such as age, body weight, genetic, accident, sport injury and extreme use such as physical work. OA can occur to a male or female, child or adult. The effects experienced by patients with OA are such as difficulty to walk, limited movement, and pain in the thin cartilage areas. Monitoring of patients' condition needs to be done to help reduce the problem and thereby enable specialists to perform the appropriate treatment. Imaging is a method used today to monitor the condition of patients with OA. Previous studies showed that MRI is a suitable method for monitoring the condition of patients with OA because of its advantages in visualising knee cartilage more clearly than other imaging methods. Thus, for segmenting the knee cartilage which as mentioned before is an important process in medical images processing, the MR images were selected based on many factors. Segmentation in this study was aimed to obtain the cartilage region to diagnose patient OA level. Various segmentation techniques have been developed by researchers in segmenting the knee cartilage region but they have been unable to segment precisely due to the thin structure of the knee cartilage, especially for patients with intermediate and severe OA. COMSeg technique was developed to segment knee cartilage, especially for those experiencing a normal and intermediate OA and try to implement it to severe OA. The development of this new technique takes into account the imaging method used, the images feature obtained so it can be suitable to process knee image and then selection of an appropriate technique to be applied to the selected images as input.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • [1]

    Choudhari S. , Biday S. (2013), Medical Image Segmentation, International Journal of Advanced and Innovative Research, 2(10), 399403.

    • Search Google Scholar
    • Export Citation
  • [2]

    Dougherty G. (2009), Digital Image Processing for Medical Applications. 1st ed., Cambridge University Press, New York.

  • [3]

    Mohd Khairul A. (2015), MR Imaging of Knee Articular Cartilage Thickness: Correlation with Severity of Osteoarthritis.

  • [4]

    Sanjeevakumar K. , Ravikumar K. M., Harini D. G. (2013), Measurement of Cartilage Thickness for Early Detection of Knee Osteoarthritis (KOA). In: Point-of-Care Healthcare Technologies (PHT), pp. 208211. IEEE, Bangalore, India.

    • Search Google Scholar
    • Export Citation
  • [5]

    Mallikarjunaswamy M. S. , Mallikarjun S. H. (2012), Knee joint articular cartilage segmentation, visualization and quantification using image processing techniques: A review. International Journal of Computer Applications, 42, 3643.

    • Search Google Scholar
    • Export Citation
  • [6]

    Tamez-Peña J. G. , et al. (2012), Unsupervised segmentation and quantification of anatomical knee features: Data from the Osteoarthritis Initiative. IEEE Trans. Biomed. Eng., 59, 11771186.

    • Search Google Scholar
    • Export Citation
  • [7]

    Bhabhor C. H. , Upadhyay A. B. (2013), Performance analysis for image segmentation of various edge detection techniques. Journal of Information, Knowledge and Research in Electronics and Communication Engineering, 2(2), 399404.

    • Search Google Scholar
    • Export Citation
  • [8]

    Chaudhary A. , Gulati T. (2013), Segmenting digital images using edge detection. International Journal of Application or Innovation in Engineering & Management, 2(5), 319323.

    • Search Google Scholar
    • Export Citation
  • [9]

    Pham D. L. , Xu C., Prince J. L. (2000), A survey of current methods in medical image segmentation. Annual Reviews of Biomedical Engineering, 2, 315337.

    • Search Google Scholar
    • Export Citation
  • [10]

    Gonzalez, R. C., Woods R. E. (2008), Digital Image Processing. 3rd edn. Pearson Prentice Hall, New Jersey.

  • [11]

    Fripp J , Crozier S, Warfield SK, Ourselin S. (2010), Automatic segmentation and quantitative analysis of the articular cartilages from magnetic resonance images of the knee. IEEE Trans. Med. Imaging, 29(1), 5564.

    • Search Google Scholar
    • Export Citation
  • [12]

    Teichtahl A. J. , Wluka A. E., Davies-Tuck M. L., Cicuttini F. M. (2008), Imaging of knee osteoarthritis. Best Pract. Res. Clin. Rheumatol., 22(6), 10611074

    • Search Google Scholar
    • Export Citation
  • [13]

    Ngo Q. L. (2011), Image processing on medical application: Automatic methods to calculate the area of an articular cartilage on a magnetic resonance image. Research Master Thesis, University of Tasmania, Australia.

    • Search Google Scholar
    • Export Citation