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Automatic Region Based Segmentation and Analysis of Lung Volumes from CT Images

Lung segmentation is an important step in modern radiological diagnostics. In this paper we propose an automatic method for lung segmentation with previous preprocessing of the lung computed tomography images. Our algorithm starts with segmentation with the initial thresholding to create binary image. After creation of the binary image, the threshold is used to set all the pixels under the selected value to the background. This will eliminate the table and other non anatomical pixels from the images. The algorithm is looking for the first pixel on the CT image, and after it is found, the region growing commence, and the lung region is segmented containing right and left lung and airway. Morphological operators are used to enhance the image edges, as well to include the missing tissue due to their high attenuation. The results were compared with the volume found by human observer’s segmentation, and for measure of similarity we used Pearson correlation coefficient, and with the P=0.996657, we can conclude that there is a significant correlation between our method and the gold truth.