A simple method for automated lung segmentation in x-ray CT images

Bin Zheng; J. Ken Leader III; Glenn S. Maitz; Brian E. Chapman; Carl R. Fuhrman; Robert M. Rogers; Frank C. Sciurba; Andrew Perez; Paul Thompson; Walter F. Good; David Gur

doi:10.1117/12.480290

15 May 2003 A simple method for automated lung segmentation in x-ray CT images

Bin Zheng, J. Ken Leader III, Glenn S. Maitz, Brian E. Chapman, Carl R. Fuhrman, Robert M. Rogers, Frank C. Sciurba, Andrew Perez, Paul Thompson, Walter F. Good, David Gur

Author Affiliations +

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480290
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

We developed and tested an automated scheme to segment lung areas depicted in CT images. The scheme includes a series of six steps. 1) Filtering and removing pixels outside the scanned anatomic structures. 2) Segmenting the potential lung areas using an adaptive threshold based on pixel value distribution in each CT slice. 3) Labeling all selected pixels ingo segmented regions and deleting isolated regions in non-lung area. 4) Labeling and filling interior cavities (e.g., pleural nodules, airway wall, and major blood vessels) inside lung areas. 5) Detecting and deleting the main airways (e.g., trachea and central bronchi) connected to the segmented lung areas. 6) Detecting and separating possible anterior or posterior junctions between the lungs. Five lung CT cases (7-10 mm in slice thickness) with variety of disease patterns were used to train or set up the classification rules in the scheme. Fifty examinations of emphysema patients were then used to test the scheme. The results were compared with the results generated from a semi-automated method with manual interaction by an expert observer. The experimental results showed that the average difference in estimated lung volumes between the automated scheme and manually corrected approach was 2.91%±0.88%. Visual examination of segmentation results indicated that the difference of the two methods was larger in the areas near the apices and the diaphragm. This preliminary study demonstrated that a simple multi-stage scheme had potential of eliminating the need for manual interaction during lunch segmentation. Hence, it can ultimately be integrated into computer schemes for quantitative analysis and diagnosis of lung diseases.

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Bin Zheng, J. Ken Leader III, Glenn S. Maitz, Brian E. Chapman, Carl R. Fuhrman, Robert M. Rogers, Frank C. Sciurba, Andrew Perez, Paul Thompson, Walter F. Good, and David Gur "A simple method for automated lung segmentation in x-ray CT images", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480290

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