A Pulmonary Vascular Segmentation Algorithm of Chest CT Images Based on Fast Marching Method

Abstract

Pulmonary vascular segmentation plays an important role in lung disease detection. In order to improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular segmentation method based on the fast marching method combined with the gray gradient and threshold is proposed in this paper. Firstly, the lung tissue is extracted from chest CT images by Maximum Between-Class Variance. Then the holes of the extracted region are filled by morphological opening and closing operations. Secondly, the points of the vascular of the middle slice of the CT images are extracted and marked as the original seed points. Finally, the seed points are spread throughout the lung tissue to extract the pulmonary vascular based on the fast marching method with the restricted grayscale value threshold and gradient. The experiments results show that the pulmonary vascular are extracted accurately by this method.