A "loop" shape descriptor and its application to automated segmentation of airways from CT scans. Issue 6 (28th May 2015)

• Record Type: Journal Article
• Authors: Pu, Jiantao; Jin, Chenwang; Yu, Nan; Qian, Yongqiang; Wang, Xiaohua; Meng, Xin; Guo, Youmin
• Is Part Of: Medical physics. Volume 42:Issue 6(2015)Part 1
• Page Start: 3076
• Page End: 3084
• Publication Date: 2015-05-28
• Subjects: computerised tomography -- image segmentation -- lung -- medical image processing -- pneumodynamics; Computed tomography -- X‐ray imaging -- Pneumodyamics, respiration -- Segmentation; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; airways -- tubular structure -- shape descriptor -- loop -- vessels -- CT; Computed tomography -- Lungs -- Databases -- Medical image artifacts -- Medical image noise -- Surface structure -- Band models -- Medical image segmentation -- Image scanners; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4921139
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images. Methods: Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed "loop" shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three‐dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concave loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation. Results: For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state‐of‐the‐art algorithms validated on the same dataset. Conclusions: The authors' quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways.