Automated Kidney Stone Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results

  • Authors

    • Sujata Navaratnam
    • Siti Fazilah
    • Valliappan Raman
    • Sundresan Perumal
    2018-10-07
    https://doi.org/10.14419/ijet.v7i4.15.21369
  • Kidney Stone, Seed Pixel Region Growing and Segmentation.

  • This research develop a computer aided diagnosis prototype for early detection of kidney stone. Once a kidney stone is diagnosed accurately, this will be useful for the patients to change their diet condition. The proposed approach is based on five stages which includes kidney image acquisition, pre-processing, segmentation, feature extraction and classification. The enhanced seed region growing segmentation depends on the extracted feature granularities. Noise may be visible and more prevalent in certain dimensions of an image, where this particular specific portion will be extracted. The segmentation process is based on the thresholds of the identified renal stone regions. The segmented stone size portion is classified based on rules; if the size is greater than 2mm, then the stone is at benign stage; if the size is greater than 5mm, then it is in malignant stage; if the size is lesser than 2mm, then this leads to absence of stone. The proposed work is implemented in MATLAB with the development of an initial prototype with the detection of stone accuracy of 92%. Based on the experimental analysis, texture feature, threshold intensity values and stone sizes are evaluated. This study will help the urologist to take decision whether there is a presence or absence of stone in early stage diagnosis and clinical decision-making.

     

     

  • References

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  • How to Cite

    Navaratnam, S., Fazilah, S., Raman, V., & Perumal, S. (2018). Automated Kidney Stone Segmentation by Seed Pixel Region Growing Approach: Initial Implementation and Results. International Journal of Engineering & Technology, 7(4.15), 43-48. https://doi.org/10.14419/ijet.v7i4.15.21369