Segmentation of lung from CT using various active contour models

The aim of the paper is to develop a region based active contour model using variational level set function for segmentation of lung. Ultimately, segmentation of parenchyma is essential for accurate diagnosis of various lung diseases. Among many imaging modalities, Computed Tomography (CT) is a pioneer to most image analysis applications. This work proposes a powerful technique named Selective Binary and Gaussian filtering-new Signed Pressure Force (SBGF-new SPF) function for segmentation of CT lung images. This process detects the external boundary of the lung and effectively stops the contour even at blurry boundaries. The proposed algorithm was compared with four different active contour models. Comparative experiments demonstrate the advantage of proposed method in terms of computation time and accurate segmented lung.