Improved approaches of automated lung segmentation on digital tomosynthesis images
AbstractFor 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.
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