Document Type: Original Paper
Master of Science in Biomedical Engineering, Department of Biomedical Systems & Medical Physics, Tehran University of Medical Sciences
Associate Professor in Biomedical Engineering, Biomedical Systems & Medical Physics Dept., Tehran University of Medical Sciences & Research Center for Science and Technology in Medicine, Tehran, Iran.
Assistant Professor in Biomedical Engineering, Electrical Engineering Dept., Sharif University of Technology, Tehran, Iran.
Associate Professor in Biomedical Engineering, Electrical Engineering Dept., Ryerson University, Toronto, Canada.
Assistant Professor, Pneumologist Consultant, Iran University of Medical Sciences, Internal Medicine Group, Tehran, Iran.
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.