Novel method using multiple strategies for accurate lung segmentation in computed tomography images

In this paper, a novel method using multiple strategies for accurate lung segmentation in CT images was proposed. The method consists of six key operation: firstly, in order to avoid noise, the input CT slice was smoothed using the guided filter. Then, the smoothed slice was transformed into a binary image using an optimized threshold. Next, a region-growing strategy was employed to extract thorax regions. Then, lung regions were segmented from the thorax regions using a seed-based random walk algorithm. The segmented lung contour was then smoothed and corrected with a curvature-based correction method on each axis slice. Finally, with the lung masks, the lung region was automatically segmented from a CT slice. Experimental results show that the proposed method accurately segmented lung regions in CT slices.