Computed tomography of ground glass nodule image based on fuzzy C-means clustering algorithm to predict invasion of pulmonary adenocarcinoma

Abstract

Early detection of lung cancer in computed tomography (CT) can significantly improve the survival rate of patients. A part of lung cancer early appeared in the form of ground glass nodule (GGN), and its early detection requires the help of a computer-aided algorithm. This study aimed to explore the CT features of GGN based on a fuzzy C-means (FCM) clustering algorithm in predicting the invasion of pulmonary adenocarcinoma. In this study, the lung parenchyma from 65 patients with GGN was segmented based improved FCM cluster algorithm. The region segmentation algorithm removed the useless area, and critical features of GGN extracted the suspicious area. After using the improved algorithm, the effect of region segmentation was superior; the simulation results show that the algorithm can remove blood vessels' “line” or branching structure well; the improved FCM had good real-time performance and robustness to noise. From the segmented GGN, the invasive adenocarcinoma (IA) had a larger size and higher CT attenuation than that of non-IA lesions. The results show that the accurate analysis of CT features of GGN based on the FCM clustering algorithm could be helpful in distinguish non-IA from IA, which is beneficial to determine strategies of follow-up or appropriate management for GGN.