Abstract

Info

Author(s):: LI Xue1; 2; ZHOU Jinzhi1; 2; MO Chunmei1; 2; YU Xi1; 2; 1. School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China 2. Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621000, China

Keywords:: Keywords: lung parenchyma U-Net automatic segmentation color feature texture feature feature fusion

Abstract:: Abstract: Objective To obtain a type of more effective feature by combining lung color features with texture features, and to accurately extract lung parenchyma by segmenting the lung CT image using improved U-Net deep learning network structure. Methods The CT image data used in the study were derived from LIDC-IDRI dataset. The color features and texture features were firstly extracted through color space conversion and high-order neighborhood statistics. Then, the mean weighted histogram was used to fuse the two types of features and the obtained features were input into the improved U-Net model for 1 000 CT scan tests, thereby achieving a complete lung parenchyma output. Results The Dice coefficient, sensitivity and specificity of the proposed method were 93%, 96% and 97%, respectively. Conclusion The proposed method which has a higher segmentation accuracy than the single feature segmentation method can effectively improve the accuracy of lung parenchyma segmentation and provide a reliable basis for the subsequent automatic diagnosis of lung diseases, thus reducing the cost of clinical diagnosis and shortening the time for diagnosis.