Segmentation of lung lobes in isotropic CT images using wavelet transformation.
Advanced multi-slice CT scanners produce isotropic CT images, which have pixel dimensions equal to their image thicknesses of 0.6 mm. Comparing to clinical standard CT images with a thickness of 2.5 - 7.0 mm, isotropic CT images have clearly visible lobar fissures. This poses a challenge for developing automatic algorithms to identify the fissure locations and curvatures. This paper presents a wavelet algorithm that allows automatic identification of the left and right oblique fissures, as well semi-automatic identification of the horizontal fissures. This algorithm took a two-stage approach: (a) adaptive fissure sweeping to find fissure regions; and (b) wavelet transform to identify the fissure locations and curvatures within these fissure regions. Tested on 8, 6 and 6 stacks of isotropic CT images for the left oblique, right oblique and horizontal fissures, respectively, the algorithm yielded an accuracy of 77.1 - 93.6% with strict evaluation criteria. This provides promising potential for developing an automatic algorithm to segment lung lobes.
Wei
Q
Q
Dept. of Electrical and Computer Engineering, University of Calgary, Calgary, AB, CANADA. qwei@ucalgary.ca
Hu
Y
Y
Gelfand
G
G
MacGregor
J H
JH
D000465:Algorithms; D016880:Anisotropy; D001185:Artificial Intelligence; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014057:Tomography, X-Ray Computed / Q000379:methods*