Knee MR image segmentation combining contextual constrained neural network and level set evolution

Haw Chang Lan, Tsai Rong Chang, Wen Ching Liao, Yi Nun Chung, Pau Choo Chung

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Tracking the patella movement trajectory during the bending process of the knee is one essential step to knee pain diagnosis. In order for tracking patella, correct segmentation of the femur and patella from the axial knee MR image is indispensable. But the strong adhesion of the soft tissue around femur and patella, the gray-level similarities of adjacent organs, and the non-uniform gray intensity due to the degradation of the magnetic propagation make the MR image segmentation challenging. In this paper, we proposed a mechanism combining contextual constraint neural network (CCNN) and level set evolution to segment the femur and patella. The segmentation can be divided into two phases. In the first phase SOM and CCNN are applied to extract initial contours of the femur and patella. Consequently in the second phase, modified level set evolution is performed, with the extracted contours as the initial zero level set contour, to accomplish the segmentation of the femur and patella. Our experimental results show that the femur and patella can be correctly segmented for tracking patella movement.

原文English
主出版物標題2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings
頁面271-277
頁數7
DOIs
出版狀態Published - 2009
事件2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Nashville, TN, United States
持續時間: 2009 3月 302009 4月 2

出版系列

名字2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009 - Proceedings

Other

Other2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2009
國家/地區United States
城市Nashville, TN
期間09-03-3009-04-02

All Science Journal Classification (ASJC) codes

  • 生物化學、遺傳與分子生物學 (全部)
  • 人工智慧
  • 計算機理論與數學

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