Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Ganglion Cyst Region Extraction from Ultrasound Images Using Possibilistic C-Means Clustering Method

  • Received : 2017.03.02
  • Accepted : 2017.03.24
  • Published : 2017.03.31
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Abstract

Ganglion cysts are benign soft tissues usually encountered in the wrist. In this paper, we propose a method to extract a ganglion cyst region from ultrasonography images by using image segmentation. The proposed method using the possibilistic c-means (PCM) clustering method is applicable to ganglion cyst extraction. The methods considered in this thesis are fuzzy stretching, median filter, PCM clustering, and connected component labeling. Fuzzy stretching performs well on ultrasonography images and improves the original image. Median filter reduces the speckle noise without decreasing the image sharpness. PCM clustering is used for categorizing pixels into the given cluster centers. Connected component labeling is used for labeling the objects in an image and extracting the cyst region. Further, PCM clustering is more robust in the case of noisy data, and the proposed method can extract a ganglion cyst area with an accuracy of 80% (16 out of 20 images).

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References

  1. W. Gude and V. Morelli, "Ganglion cysts of the wrist: pathophysiology, clinical picture, and management," Current Reviews in Musculoskeletal Medicine, vol. 1, no. 3-4, pp. 205-211, 2008. https://doi.org/10.1007/s12178-008-9033-4
  2. V. Freire, H. Guerini, R. Campagna, L. Moutounet, C. Dumontier, A. Feydy, and J. L. Drape, J. L. "Imaging of hand and wrist cysts: a clinical approach," American Journal of Roentgenology, vol. 199, no. 5, pp. W618-W628, 2012. https://doi.org/10.2214/AJR.11.8087
  3. A. M. Plate, S. J. Lee, G. Steiner, & M. A. Posner, "Tumorlike lesions and benign tumors of the hand and wrist," Journal of the American Academy of Orthopaedic Surgeons, vol. 11, no. 2, pp. 129-141, 2003. https://doi.org/10.5435/00124635-200303000-00007
  4. K. B. Kim and C. W. Kim, "Quantification of hepatorenal index for computer-aided fatty liver classification with self-organizing map and fuzzy stretching from ultrasonography," BioMed Research International, vol. 2015, article no. 535894, pp. 1-9, 2015.
  5. N. H. Mahmood, M. R. Razif, and M. T. Gany, "Comparison between median, unsharp and wiener filter and its effect on ultrasound stomach tissue image segmentation for pyloric stenosis," International Journal of Applied Science and Technology, vol. 1, no. 5, pp. 218-226, 2011.
  6. C. K. Yee, "Median filter using NIOS II processor with sort hardware accelerator," University Technology Malaysia, 2009.
  7. S. Nascimento, S. (2005). Fuzzy Clustering via Proportional Membership Model. Amsterdam: IOS Press.
  8. A. W. Malik, B. Thornberg, M. Imran, and N. Lawal, "Hardware architecture for real-time computation of image component feature descriptors on a FPGA," International Journal of Distributed Sensor Networks, vol. 2014, article no. 815378, 2014.
  9. L. He, Y. Chao, and K. Suzuki, "A run-based two-scan labeling algorithm," IEEE Transactions on Image Processing, vol. 17, no. 5, pp. 749-756, 2008. https://doi.org/10.1109/TIP.2008.919369

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