Abstract
Estimates of lung air volumes are often made in the clinical assessment of respiratory diseases using high‐resolution computed tomography (HRCT). Most algorithms are based on a thresholding of CT Hounsfield unit (HU) values using the “Otsu” method. This algorithm was developed for character recognition tasks in digital image processing chooses an algorithm that separates air from soft tissue by maximizing the ratio of intra‐class to inter‐class variances in voxel HU values. While the Otsu algorithm is widely used, it has not been experimentally validated. We tested the Otsu algorithm in a lung phantom containing solid spheres of known diameter using both HRCT and small‐animal uCT images. We found that air volumes could be determined using HRCT with an inaccuracy of less than 10% when sphere diameters were greater than 3 mm. Spheres of 2 and 1‐mm diameter gave errors of 38% and 75% respectively. It is concluded that thresholding of HRCT using the Otsu method can be accurate in regions containing air spaces larger than 3 mm, but not in regions containing alveoli as they are typically much less than a mm in diameter.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
