Title :
An Efficient Method of Automatic Pulmonary Parenchyma Segmentation in CT Images
Author :
Zhaoxue Chen ; Xiwen Sun ; Shengdong Nie
Author_Institution :
Univ. of Shanghai for Sci. & Technol., Shanghai
Abstract :
Based on special distributing characteristics of pixel intensity in lung CT images, an efficient lung segmentation method is introduced. Associating approach of image threshold with fast region flood Ailing technique, this method can extract pulmonary parenchyma from CT images simply. After a preprocessing step for noise removal, it segments the lung CT image slice utilizing a threshold method at first, and then applies a fast and simple method to finish flood filling of the non-lung area. In the following steps, the lung area can be extracted automatically after an erosion operation and an area-Altering step. The presented experiment results have proved its validity.
Keywords :
cancer; computerised tomography; image segmentation; lung; medical image processing; noise; automatic pulmonary parenchyma segmentation; erosion operation; fast region flood filling technique; image extraction; image threshold; lung CT images; lung cancer; lung segmentation method; noise removal; Biomedical image processing; Biomedical imaging; Cancer; Computed tomography; Filling; Floods; Image segmentation; Lungs; Medical diagnostic imaging; Sun; Algorithms; Artificial Intelligence; Humans; Imaging, Three-Dimensional; Lung; Pattern Recognition, Automated; Radiographic Image Enhancement; Radiographic Image Interpretation, Computer-Assisted; Reproducibility of Results; Sensitivity and Specificity; Tomography, X-Ray Computed;
Conference_Titel :
Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE
Conference_Location :
Lyon
Print_ISBN :
978-1-4244-0787-3
DOI :
10.1109/IEMBS.2007.4353601