Automated Lung Field Segmentation in CT images using Mean Shift Clustering and Geometrical Features

Lung field segmentation is a prerequisite for development of automated computer aided diagnosis system from chest computed tomography (CT) scans. Intensity based algorithm such as mean shift (MS) segmentation on CT images for delineation of lung field is reported as the best technique in terms of accuracy and speed in the literature. However, in presence of high dense abnormalities, accurate and automated delineation of lung field becomes difficult. So an improved lung field segmentation using mean shift clustering followed by geometric property based techniques such as lung region of interest (ROI) created from symmetric centroid map of two normal subjects, false positives (FP) reduction module (using eccentricity, solidity, area, centroid features) and false negatives (FN) reduction module (using overlap feature between clusters from MS label map and convex hull of costal lung) is proposed. The performance of the proposed algorithm is validated on images obtained from Lung Image Database Consortium (LIDC) - Image Database Resource Initiative (IDRI) public database of 17 subjects containing nodular patterns and from local database of 26 subjects containing interstitial lung disease (ILD) patterns. The proposed algorithm has achieved mean Modified Hausdorff Distance (MHD) in mm of 1.47 +/- 4.31, Dice Similarity Coefficient (DSC) of 0.9854 +/- 0.0288, sensitivity of 0.9771 +/- 0.0433, specificity of 0.9991 +/- 0.0014 for 133 normal images from 32 subjects and MHD in mm of 6.23 +/- 9.00, DSC of 0.8954 +/- 0.1498, sensitivity of 0.8468 +/- 0.1908, specificity of 0.9969 +/- 0.0061 for 296 abnormal images from 43 subjects.