Automatic segmentation of chondroblastoma from X-ray images using active contour and levelset method

  • Authors

    • P Y. Muhammed Anshad
    • Dr S.S. Kumar
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.11853
  • Chondroblastoma, computer aided methods, segmentation, active contour, levelset method, CAD,

  • Abstract

    Chondroblastoma is a benign but locally aggressive bone tumor found usually in the age below 25 years. Chondroblastoma is a destructive type of lesion with a thin radio dense border which is normally seen in the epiphysis of long bones. The benign tumors have similarities in pathology and could be related with histogenic similarity. This tumor reduces the strength of affected bone and may leads to death if not treated early. Chondroblastoma can be diagnosed from X-ray/CT/MRI images and the treatment is its removal by surgical methods. Diagnosis of Chondroblastoma is difficult due to the similarities with other benign tumors like chondromyxoid fibroma. To reduce diagnostic errors, computer aided methods can adopt. This work focuses on automatic segmentation of Chondroblastoma using active contour and level set method which gives better segmentation results and a mild stone to CAD design.

     

  • References

    1. [1] Muhammed Anshad PY & Kumar SS, “Segmentation of Chondroblastoma based on Active Contour and Level Set Method†Indian Journal of Science and Technology, Vol.8, No.24, (2015).

      [2] Kass M, Witkin A & Terzopoulos D, “Snakes, active contour modelâ€, International Journal of Computer Vision, (1988), pp.321–331.

      [3] Zhang S, Cui Z, Zhang L, Zhu X, Xu T, Han Y & Wan M, “Feasibility of acoustic evaluation of thermal lesions at bone-soft tissue interface of an ex vivo bovine bone exposed to high-intensity focused ultrasoundâ€, IEEE International Ultrasonics Symposium (IUS), (2015, pp.1-4.

      [4] Hojjattolesami SA & Kittler J, “Region Growing - A New Approachâ€, Geodesic active contours, (1995), pp.694-699.

      [5] Sethian J.A. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge university press, (1999).

      [6] Atkins MS & Mackiewich BT, “Fully automated hybrid segmentation of the brainâ€, Bankman, editor. Handbook of medical Imaging, Processing and Analysis. Academic Press, (2000), pp.171–183.

      [7] Serra J. Image analysis and mathematical morphology. Academic Press, (1982).

      [8] Droske M, Meyer B, Rumpf M & Schaller K, “An adaptive level set method for medical image segmentationâ€, Proceedings of the Annual Symposium on Information Processing in Medical Imaging, (2001).

      [9] McNitt-Gray MF, Huang HK & Sayre JW, “Feature selection in the pattern classification problem of digital chest radiograph segmentationâ€, IEEE Trans Med Imag., Vol.14, No.3, (1995), pp.537–47.

      [10] Kimmel R, Amir A & Bruckstein A, “Finding shortest paths on surfaces using level set propagationâ€, IEEE Trans Pattern Anal Mach Intell., (1995), pp.635–40.

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  • How to Cite

    Y. Muhammed Anshad, P., & S.S. Kumar, D. (2018). Automatic segmentation of chondroblastoma from X-ray images using active contour and levelset method. International Journal of Engineering & Technology, 7(2.21), 140-143. https://doi.org/10.14419/ijet.v7i2.21.11853

    Received date: 2018-04-21

    Accepted date: 2018-04-21

    Published date: 2018-04-20