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Volume 41 Issue 6
Jun.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(6): 982-988.
CAO Chunhong, AI Liang, XU Guangxinget al. Head bone tissue extraction algorithm based on CTA image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(6): 982-988. doi: 10.13700/j.bh.1001-5965.2014.0502(in Chinese)
Citation: CAO Chunhong, AI Liang, XU Guangxinget al. Head bone tissue extraction algorithm based on CTA image[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(6): 982-988. doi: 10.13700/j.bh.1001-5965.2014.0502(in Chinese)
shu

Head bone tissue extraction algorithm based on CTA image

doi: 10.13700/j.bh.1001-5965.2014.0502
  • 1.

    College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

  • 2. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang 110819, China

  • 3. Information Computing Institute, Shenyang Railway Bureau, Shenyang 110001, China

  • Received Date: 11 Aug 2014
  • Publish Date: 20 Jun 2015
    Abstract
  • Vascular tissue and bone tissue cannot be clearly separated based solely on grayscale information in images of computed tomography angiography (CTA). The algorithm based on the growth of three-dimensional (3D) region improved bone tissue outside the bone contour extraction and the extraction algorithm based on improved Snake model combined with the characteristics of CTA grayscale images were proposed. Combining the knowledge of probability theory to improve the accuracy of determining condition of the region growing, fast skeletal regional seed extraction method of 3D region growing was proposed. It made it possible to obtain more accurate bone tissue area. After the Snake model was selected and some improvements were made to the model, energy image information items were increased, so that the model can better solve the current problems. Finally, the experimental results were given and compared with results from the traditional algorithm. It is confirmed the proposed segmentation of bone tissue extraction algorithm works well.

     

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