Open AccessAn unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing
Author(s) -
He Ren,
Lingxiao Zhou,
Gang Liu,
Xueqing Peng,
Wei Shi,
Huilin Xu,
Fei Shan,
Lei Liu
Publication year - 2020
Publication title -
quantitative imaging in medicine and surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.766
H-Index - 21
eISSN - 2223-4292
pISSN - 2223-4306
DOI - 10.21037/qims.2019.12.02
Subject(s) - computer science , segmentation , nodule (geology) , region growing , robustness (evolution) , artificial intelligence , image segmentation , pattern recognition (psychology) , medical imaging , computer vision , scale space segmentation , paleontology , biochemistry , chemistry , gene , biology
Nowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation.