Improved approaches of automated lung segmentation on digital tomosynthesis images

Abstract

For lung screening the most common method is chest radiography, which produces summa­tion images without giving any depth infor­mation about the lung. Computed Tomography (CT) creates excellent slice images, which give volume data that makes CT a more sensitive nodule detection sys­tem. However, CT has disadvantages, it is too expensive and its x-ray emission is too high to be used as an everyday screening method. Digital tomosynthesis (DTS), as a relatively new chest imaging modality, can be posi­tioned between chest radiography and CT. While it produces slice images of the chest similarly to CT, its slice thickness is larger, it creates a bit more blurred slices, it has much lower radiation than CT. This blur­ring makes it hard to segment the lung areas automatically, which is essential for an efficient Computer-aided Diagnosis system. The paper proposes a com­bined method, which starts from a previously pub­lished approach, extends it using snake methods and adjacent images’ segmentation information to improve lung segmentation. Experiments show that the combination of methods reduces the incorrectly seg­mented lung region.