Lung Segmentation Based on Customized Active Shape Model from Digital Radiography Chest Images

In this paper, a customized active shape model to extract lungs from radiography chest images was proposed and validated. Firstly, the average active shape model, gray-scale projection and affine registration were employed to attain the initial lung contours. Secondly, a new objective function with constraints of distance and edge was proposed to push the vertices of active shape model to the real lung edge, pull the vertices out of the stomach gas regions, and have a more balanced distance distribution of vertices. Finally, multi-resolution representation and optimization were employed to attain fast optimization. Experimental results on a public database of 247 images showed that the proposed algorithm could achieve an average accuracy of 94.7%, which is 4.4% better than the traditional active shape model and 2.7% better than the active shape model with local invariant features.