Optimal Graph Based Segmentation Using Flow Lines with Application to Airway Wall Segmentation

• Published in: Information Processing in Medical Imaging Vol. 22; pp. 49 - 60
• Main Authors: Petersen, Jens, Nielsen, Mads, Lo, Pechin, Saghir, Zaigham, Dirksen, Asger, de Bruijne, Marleen
• Format: Book Chapter Journal Article
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2011
• Series: Lecture Notes in Computer Science
• Subjects: airway walls; Algorithms; Artificial Intelligence; flow lines; graph; Humans; Lung - diagnostic imaging; Pattern Recognition, Automated - methods; Radiographic Image Enhancement - methods; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Segmentation; Sensitivity and Specificity; Tomography, X-Ray Computed - methods
• ISBN: 3642220916; 9783642220913
• ISSN: 0302-9743; 1011-2499; 1611-3349
• DOI: 10.1007/978-3-642-22092-0_5

This paper introduces a novel optimal graph construction method that is applicable to multi-dimensional, multi-surface segmentation problems. Such problems are often solved by refining an initial coarse surface within the space given by graph columns. Conventional columns are not well suited for surfaces with high curvature or complex shapes but the proposed columns, based on properly generated flow lines, which are non-intersecting, guarantee solutions that do not self-intersect and are better able to handle such surfaces. The method is applied to segment human airway walls in computed tomography images. Comparison with manual annotations on 649 cross-sectional images from 15 different subjects shows significantly smaller contour distances and larger area of overlap than are obtained with recently published graph based methods. Airway abnormality measurements obtained with the method on 480 scan pairs from a lung cancer screening trial are reproducible and correlate significantly with lung function.