Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques.
OBJECTIVE
The aim of this study was to develop a novel algorithm for segmenting lung nodules on three-dimensional (3D) computed tomographic images to improve the performance of computer-aided diagnosis (CAD) systems.


METHODS
The database used in this study consists of two data sets obtained from the Lung Imaging Database Consortium. The first data set, containing 23 nodules (22% irregular nodules, 13% nonsolid nodules, 17% nodules attached to other structures), was used for training. The second data set, containing 64 nodules (37% irregular nodules, 40% nonsolid nodules, 62% nodules attached to other structures), was used for testing. Two key techniques were developed in the segmentation algorithm: (1) a 3D extended dynamic programming model, with a newly defined internal cost function based on the information between adjacent slices, allowing parameters to be adapted to each slice, and (2) a multidirection fusion technique, which makes use of the complementary relationships among different directions to improve the final segmentation accuracy. The performance of this approach was evaluated by the overlap criterion, complemented by the true-positive fraction and the false-positive fraction criteria.


RESULTS
The mean values of the overlap, true-positive fraction, and false-positive fraction for the first data set achieved using the segmentation scheme were 66%, 75%, and 15%, respectively, and the corresponding values for the second data set were 58%, 71%, and 22%, respectively.


CONCLUSIONS
The experimental results indicate that this segmentation scheme can achieve better performance for nodule segmentation than two existing algorithms reported in the literature. The proposed 3D extended dynamic programming model is an effective way to segment sequential images of lung nodules. The proposed multidirection fusion technique is capable of reducing segmentation errors especially for no-nodule and near-end slices, thus resulting in better overall performance.
Center for Biomedical Imaging and Bioinformatics, Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Qian
Q
Wang
Enmin
E
Song
Renchao
R
Jin
Ping
P
Han
Xiaotong
X
Wang
Yanying
Y
Zhou
Jianchao
J
Zeng
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity