MGRG-Morphological Gradient Based 3D Region Growing Algorithm for Airway Tree Segmentation in Image Guided Intervention Therapy
Accurate surgical planning and guidance plays an important role in successful implementation of image guided intervention. In interventional lung cancer diagnosis and treatments, precise segmentation of airway trees from lung CT images provides crucial visualization for preoperative planning and intraoperative guidance to avoid major trachea injury. While 3D region growing can segment main the parts of an airway tree (trachea, left and right main bronchus, as well as bronchi), the method fails at bronchiole segmentation and is not robust. Mathematical morphology is an anatomical detective. In this paper, we propose a morphological gradient based region growing (MGRG) algorithm to overcome the intensity inhomogeneity, and improve the robustness of 3D region growing on extraction of bronchioles. The MGRG algorithm is validated using lung CT images, and results show that it is able to segment bronchioles, and outperforms the traditional region growing method on airway tree segmentation.
Dezhi Gao Xin Gao Caifang Ni Tao Zhang
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, C department of medical imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Ac department of interventional radiology, First Affiliated Hospital of Soochow University, Suzhou, CHI Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, C
国际会议
2011 International Symposium on Bioelectronics and Bioinformatics(第二届国际生物医学电子学与生物信息学学术会议 ISBB 2011)
苏州
英文
76-79
2011-11-03（万方平台首次上网日期，不代表论文的发表时间）