@article { author = {Yousefi Rizi, Fereshteh and Ahmadian, Alireza and FatemiZadeh, Emad and Alirezaie, Javad and Rezaie, Nader}, title = {Intrathoracic Airway Tree Segmentation from CT Images Using a Fuzzy Connectivity Method}, journal = {Iranian Journal of Medical Physics}, volume = {6}, number = {1}, pages = {71-83}, year = {2009}, publisher = {Mashhad University of Medical Sciences}, issn = {2345-3672}, eissn = {2345-3672}, doi = {10.22038/ijmp.2009.7392}, abstract = {Introduction: Virtual bronchoscopy is a reliable and efficient diagnostic method for primary symptoms of lung cancer. The segmentation of airways from CT images is a critical step for numerous virtual bronchoscopy applications. Materials and Methods: To overcome the limitations of the fuzzy connectedness method, the proposed technique, called fuzzy connectivity - fuzzy C-mean (FC-FCM), utilized the FCM algorithm. Then, hanging-togetherness of pixels was handled by employing a spatial membership function. Another problem in airway segmentation that had to be overcome was the leakage into the extra-luminal regions due to the thinness of the airway walls during the process of segmentation. Results:   The result shows an accuracy of 92.92% obtained for segmentation of the airway tree up to the fourth generation. Conclusion:  We have presented a new segmentation method that is not only robust regarding the leakage problem but also functions more efficiently than the traditional FC method. }, keywords = {Airway tree segmentation,Fuzzy connectivity,Fuzzy C-Mean}, url = {https://ijmp.mums.ac.ir/article_7392.html}, eprint = {https://ijmp.mums.ac.ir/article_7392_86153d8bb02439185da8616ef5719abd.pdf} }