title: [A probability segmentation algorithm for lung nodules based on three-dimensional features].
abstract: This paper presents a probability segmentation algorithm for lung nodules based on three-dimensional features. Firstly, we computed intensity and texture features in region of interest (ROI) pixel by pixel to get their feature vector, and then classified all the pixels based on their feature vector. At last, we carried region growing on the classified result, and got the final segmentation result. Using the public Lung Imaging Database Consortium (LIDC) lung nodule datasets, we verified the performance of proposed method by comparing the probability map within LIDC datasets, which was drawn by four radiology doctors separately. The experimental results showed that the segmentation algorithm using three-dimensional intensity and texture features would be effective.
authors.forename: Jia
authors.initials: J
authors.lastname: Song
authors.forename: Shengdong
authors.initials: S
authors.lastname: Nie
authors.forename: Yuanjun
authors.initials: Y
authors.lastname: Wang
authors.forename: Wen
authors.initials: W
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D021621:Imaging, Three-Dimensional; D008168:Lung / Q000473:pathology*; D011336:Probability