Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation

Lung nodule segmentation is an important pre-processing step for analysis of solitary pulmonary nodules in computed tomography (CT) imaging. However, the previous nodule segmentation methods cannot segment the cavitary nodules entirely. To address this problem, an automated segmentation method based on self-generating neural networks and particle swarm optimisation (PSO) is proposed to ensure the integrity of cavitary nodule segmentation. Our segmentation method first roughly segments the image using a general region-growing method. Thereafter, the PSO-self-generating neural forest (SGNF)-based classification algorithm is used to cluster regions. Finally, grey and geometric features are utilised to identify the nodular region. Experimental results show that our method can achieve an average pixel overlap ratio of 88.9% compared with manual segmentation results. Moreover, compared with existing methods, this algorithm has higher segmentation precision and accuracy for cavitary nodules.