Document Type : Full Research Paper

Authors

• Fereshte Yousefi Rizi ¹
• Alireza Ahmadian ²
• Javad Alirezaie ³
• Emadoddin Fatemizadeh ⁴
• Nader Rezaei ⁵

¹ M.Sc Graduated, Biomedical Systems & Biophysics Department, Research Center for Science and Technology in Medicine (MI Group), Tehran University of Medical Sciences

² Associate Professor, Biomedical Systems & Biophysics Department, Research Center for Science and Technology in Medicine (MI Group), Tehran University of Medical Sciences

³ Associate Professor, Department of Electrical and Computer Engineering, Engineering and Applied Science School, Ryerson University

⁴ Assistant Professor, Department of Medical Engineering, Electrical Engineering School, Sharif University of Technology

⁵ Assistant Professor, Pneumologist Consultant, Medical School, Iran University of Medical Sciences

Abstract

Partial volume effect and image noise greatly decrease the visibility of the airway wall. Another dilemma with airway segmentation methods, which significantly influences their accuracy, is the leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. A solution to this problem in the previous methods was based on leak detection and reduction by adjusting the segmentation parameters and performing the whole segmentation process, which is very time consuming and demands user interaction. The new strategy presented here is to prevent the leakage by taking the advantage of the fact that the airway branches are cylindrically shaped objects. This has been achieved by introducing a new mathematical shape optimization approach embedded in FC-FCM algorithm to retain the cylindrical properties of the airway branches during the segmentation process. The main role of this optimization approach is to detect and correct the underlying voxels which belonging to the airway by satisfying both conditions of the fuzzy connectivity and shape features.  The proposed FC-FCM algorithm was first applied on four data sets each containing 430 CT images of CT images of airway tree. The result showed an accuracy of 93% obtained for segmentation of the airway tree up to the fourth generation. We then applied OPT-FC-FCM algorithm to segment the airway tree with optimization process up to the sixth generation of airway. The result proves the ability of our proposed method to complete a visually acceptable segmentation of airway trees with no leakage. The number of detected branches was found 65 (4 times of those obtained by using just the FC-FCM method).

Keywords

• Airway segmentation
• Fuzzy connectivity
• shape optimization
• Center estimation

Main Subjects

• Biological Computer Modeling / Biological Computer Simulation