Automated lung segmentation in magnetic resonance images

William F. Sensakovic; Samuel G. Armato III; Adam Starkey

doi:10.1117/12.595973

29 April 2005 Automated lung segmentation in magnetic resonance images

William F. Sensakovic, Samuel G. Armato III, Adam Starkey

Proceedings Volume 5747, Medical Imaging 2005: Image Processing; (2005) https://doi.org/10.1117/12.595973
Event: Medical Imaging, 2005, San Diego, California, United States

Abstract

Segmentation of the lungs within magnetic resonance (MR) scans is a necessary preprocessing step in the computerized analysis of thoracic MR images. This task is complicated by potentially significant cardiac and pulmonary motion artifacts, partial volume effect, and morphological deformation from disease. We have developed an automated segmentation method to account for these complications. First, the thorax is segmented using a threshold obtained from analysis of the cumulative gray-level histogram constructed along a diagonal line through the center of the image. Next two separate lung-thresholded images are created. The first lung-thresholded image is created using histogram-based gray-level thresholding techniques applied to the segmented thorax. To include lung areas that may be adversely affected by artifact or disease, a second lung-thresholded image is created by applying a grayscale erosion operator to the first lung-thresholded image. After a rolling ball filter is applied to the lung contour to eliminate non-lung pixels from the thresholded lung regions, a logical OR operation is used to combine the two lung-thresholded images into the final segmented lung regions. Modifications to this approach were required to properly segment sections in the lung bases. In a preliminary evaluation, the automated method was applied to 10 MR scans, an observer evaluated the segmented lung regions using a five-point scale (“highly accurate segmentation” to “highly inaccurate segmentation”). Eighty-five percent of the segmented lung regions were rated as highly or moderately accurate.

Citation Download Citation

William F. Sensakovic, Samuel G. Armato III, and Adam Starkey "Automated lung segmentation in magnetic resonance images", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); https://doi.org/10.1117/12.595973

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