Pulmonary nodule detection in CT images based on shape constraint CV model.
OBJECTIVE
Accurate detection of pulmonary nodules remains a technical challenge in computer-aided diagnosis systems because some nodules may adhere to the blood vessels or the lung wall, which have low contrast compared to the surrounding tissues. In this paper, the analysis of typical shape features of candidate nodules based on a shape constraint Chan-Vese (CV) model combined with calculation of the number of blood branches adhered to nodule candidates is proposed to reduce false positive (FP) nodules from candidate nodules.


METHODS
The proposed scheme consists of three major stages: (1) Segmentation of lung parenchyma from computed tomography images. (2) Extraction of candidate nodules. (3) Reduction of FP nodules. A gray level enhancement combined with a spherical shape enhancement filter is introduced to extract the candidate nodules and their sphere-like contour regions. FPs are removed by analysis of the typical shape features of nodule candidates based on the CV model using spherical constraint and by investigating the number of blood branches adhered to the candidate nodules. The constrained shapes of CV model are automatically achieved from the extracted candidate nodules.


RESULTS
The detection performance was evaluated on 127 nodules of 103 cases including three types of challenging nodules, which are juxta-pleural nodules, juxta-vascular nodules, and ground glass opacity nodules. The free-receiver operating characteristic (FROC) curve shows that the proposed method is able to detect 88% of all the nodules in the data set with 4 FPs per case.


CONCLUSIONS
Evaluation shows that the authors' method is feasible and effective for detection of three types of nodules in this study.
College of Mathematics and Computer Science, Hebei University, Baoding 071002, China.
Bing
B
Wang
College of Mathematics and Computer Science, Hebei University, Baoding 071002, China.
Xuedong
X
Tian
Hebei Geological Laboratory, Baoding 071000, China and Multi-disciplinary Research Center, Hebei University, Baoding 071002, China.
Qian
Q
Wang
Hebei University Affiliated Hospital, Baoding 071002, China.
Ying
Y
Yang
Department of Cardiology, Peking Union Medical College Hospital, Peking 100005, China.
Hongzhi
H
Xie
Department of Cardiology, Peking Union Medical College Hospital, Peking 100005, China.
Shuyang
S
Zhang
Multi-disciplinary Research Center, Hebei University, Baoding 071002, China and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Lixu
L
Gu
D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging / Q000473:pathology; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology; D014057:Tomography, X-Ray Computed