Automatic CT image segmentation of the lungs with an iterative Chan-Vese algorithm

Abstract

Lung segmentation is an important task for quantitative lung CT image analysis and computer aided diagnosis. However, accurate and automated lung CT image segmentation may be made difficult by the presence of the abnormalities. Since many lung diseases change tissue density, resulting in intensity changes in the CT image data, intensity only segmentation algorithms will not work for most pathological lung cases. In this paper, a modified Chan-Vese algorithm is proposed for image segmentation, which is based on the similarity between each point and center point in the neighborhood. This algorithm can capture the details of local region to realize the image segmentation in gray-level heterogeneous area. Experimental results show that this method can segment the lungs CT image with high accuracy, adapt ability and more stable performance compared with the traditional Chan-Vese model.