[Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics].
This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. First, a modified scale-invariant feature transform (SIFT) local descriptor is used to characterize the image features in the vicinity of each pixel, so that the deformable model deforms in a way that seeks for the region with similar SIFT local descriptors; second, the deformable model is constrained by both population-based and patient-specific shape statistics. At first, population-based shape statistics plays an leading role when the number of serial images is small, and gradually, patient-specific shape statistics plays a more and more important role after a sufficient number of segmentation results on the same patient have been obtained. The proposed deformable model can adapt to the shape variability of different patients, and obtain more robust and accurate segmentation results.
Shi
Yong-hong
YH
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240.
Qi
Fei-hu
FH
Luan
Hong-xia
HX
Wu
Guo-rong
GR
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000175:diagnosis; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity