Automatic Segmentation of Lung Fields on Chest Radiographic Images

Carreira, Marı́a J.; Cabello, D.; Mosquera, A.

doi:10.1006/cbmr.1999.1510

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…In lung segmentation method developed by Duryea et al, 10 the gray-scale of chest radiography was first remapped to improve the image contrast between the lung field and surrounding tissue, and the outlines of lungs were then determined by identifying the edge points with greatest contrast on the horizontal profiles. Carreira et al 11 and Brown et al 12 developed rule-based lung segmentation schemes, in which the image features and clinical information were combined and used for the identification of lung regions. In studies of McNitt-Gray et al 13 and Tsujii et al, 14 artificial neural network technique was employed for lung segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automated lung segmentation in digital chest tomosynthesis

Wang

Dobbins

2012

Med. Phys.

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Purpose: The purpose of this study was to develop an automated lung segmentation method for computerized detection of lung nodules in digital chest tomosynthesis. Methods: The authors collected 45 digital tomosynthesis scans and manually segmented reference lung regions in each scan to assess the performance of the method. The authors automated the technique by calculating the edge gradient in an original image for enhancing lung outline and transforming the edge gradient image to polar coordinate space. The authors then employed a dynamic programming technique to delineate outlines of the unobscured lungs in the transformed edge gradient image. The lung outlines were converted back to the original image to provide the final segmentation result. The above lung segmentation algorithm was first applied to the central reconstructed tomosynthesis slice because of the absence of ribs overlapping lung structures. The segmented lung in the central slice was then used to guide lung segmentation in noncentral slices. The authors evaluated the segmentation method by using (1) an overlap rate of lung regions, (2) a mean absolute distance (MAD) of lung borders, (3) a Hausdorff distance of lung borders between the automatically segmented lungs and manually segmented reference lungs, and (4) the fraction of nodules included in the automatically segmented lungs. Results: The segmentation method achieved mean overlap rates of 85.7%, 88.3%, and 87.0% for left lungs, right lungs, and entire lungs, respectively; mean MAD of 4.8, 3.9, and 4.4 mm for left lungs, right lungs, and entire lungs, respectively; and mean Hausdorrf distance of 25.0 mm, 25.5 mm, and 30.1 mm for left lungs, right lungs, and entire lungs, respectively. All of the nodules inside the reference lungs were correctly included in the segmented lungs obtained with the lung segmentation method. Conclusions: The method achieved relatively high accuracy for lung segmentation and will be useful for computer-aided detection of lung nodules in digital tomosynthesis.

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