关键词: 气管树分割, 区域生长, 模糊连接度, CT图像

Abstract: Considering the noise, intensity inhomogeneity and boundary fuzzy in CT image, it is difficult to segment pulmonary airway tree accurately. To improve the segmentation results, 3D multi-seeded fuzzy connectedness algorithm is proposed. Firstly, the region of interest is defined by extracting the lung parenchyma with a global threshold and a morphological closing operation. Secondly, the trachea and big bronchi are pre-segmented using an improved region growing method on basis of an iterative hysteresis threshold, and a local volume explosion index is adopted to suppress the lateral leakage. Then, branch points are extracted from the skeleton which is extracted and pruned from the pre-segmentation results, and these points as the new seed points for subsequent segmentation. Finally, the fuzzy connectedness between the seed and any point is calculated by constructing affinity function according to intensity homogeneity and tubular structure characteristics of the trachea. Besides, the fuzzy connectedness is segmented by choosing an appropriate threshold. The algorithms are tested with the publicly available data of the EXACT’09 challenge, and the quantitative evaluation is conducted with the node number, branch number and branch ratio on the 20 test CT cases by comparing with a manual reference. The proposed method is able to detect more than half of branches in the reference and the mean numbers of detected branches reach 59.7% under a relatively low leakage rate. The experimental results show that the method has more accurate segmentation results.

Key words: airway tree segmentation, region growing, fuzzy connectedness, CT image