Segmentation of Pulmonary Nodules Using a GMM Fuzzy C-Means Algorithm

• Published in: IEEE access Vol. 8; pp. 37541 - 37556
• Main Authors: Li, Xiangxia, Li, Bin, Liu, Fang, Yin, Hua, Zhou, Feng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Clustering; Clustering algorithms; Computed tomography; Datasets; FCM; Gaussian mixture model; Image segmentation; Indexes; Inhomogeneity; Lung cancer; Medical imaging; Nodules; Noise intensity; Nonhomogeneous media; pulmonary nodule; random walker; Segmentation; Shape; Spatial data
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2968936

Segmentation of pulmonary nodule in thoracic computed tomography (CT) plays an important role in the computer-aided diagnosis (CAD) and clinical practices. However, segmentation of pulmonary nodules still remains a challenging task due to the presence of intrinsic noise, low contrast, intensity-profile inhomogeneity, variable sizes and shapes. Many variants and extensions of fuzzy C-mean (FCM) clustering algorithm have been developed to preserve image details as well as suppress image noises. However, these variants overemphasize the importance of the spatial information and neglect the role of the prior knowledge. To address this problem, a GMM fuzzy C-means (GMMFCM) algorithm is proposed for the segmentation of pulmonary nodules in this paper. A novel local similarity measure is defined by using local spatial information and GMM statistical information. A neighboring term is added to the energy function of traditional fuzzy C-mean algorithm. A superpixel-based random walker is proposed to segment pulmonary parenchyma, which reduces the computational complexity and improves the segmentation performance. Experiments performed on the LIDC dataset and the GHGZMCPLA dataset demonstrate that the segmentation performance of proposed GMMFCM algorithm is superior to the state-of-the-art algorithms.