Research on Fast FCM Pulmonary Nodule Segmentation Algorithm Using Improved Self-adaption

The key problem of computer-aided diagnosis (CAD) of the lung cancer is to segment the pathological changed tissues fast and accurately.As pulmonary nodules are potential manifestation of the lung cancer, we propose a fast fuzzy C-means clustering pulmonary nodules segmentation method that can effectively improve the local neighborhood self-adaptability.First, the algorithm constructs two-dimensional vectors of spatial relations between pixels and neighborhood, for getting the statistical distribution pattern of different vector.Then, the enhanced spatial function considers both the grayscale similarity and spatial similarity, updates the cluster centers iteratively and fuzzy membership degree, for adjusting the effect of membership degree from the neighborhood pixels.Finally, algorithm analysis shows improvement for iteration computing efficiency and local self-adaption.The experimental results show that the proposed algorithm can achieve more accurate segmentation of vascular adhesion, pleural adhesion and ground glass opacity (GGO) pulmonary nodules, and performs better in convergence efficiency and error rates.