Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images

• Published in: IEEE transactions on medical imaging Vol. 20; no. 6; pp. 490 - 498
• Main Authors: Hu, S., Hoffman, E.A., Reinhardt, J.M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arteries; Biological and medical sciences; Computational methods; Computed tomography; Dynamic programming; Human computer interaction; Humans; Image analysis; Image segmentation; Image sequence analysis; Imaging, Three-Dimensional; Intervals; Investigative techniques, diagnostic techniques (general aspects); Lung - anatomy & histology; Lung - diagnostic imaging; Lungs; Manuals; Medical imaging; Medical sciences; Morphological operations; Pulmonary arteries; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Respiratory system; Segmentation; Standard deviation; Studies; Testing; Tissue; Tomography, X-Ray Computed - methods; X ray analysis; X-ray imaging; X-rays; Human; Evaluation; Image recognition; 3D imaging; Radiodiagnosis; Segmentation; Image processing; Lung; Respiratory system; Algorithm; Automated processing; Image analysis; Medical imagery; Computerized axial tomography; Diagnostic aid
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.929615

Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with these obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region. The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. The authors present results by comparing their automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75%/spl plusmn/2.29% (mean/spl plusmn/standard deviation).