TY - GEN
T1 - Learning longitudinal deformations for adaptive segmentation of lung fields from serial chest radiographs
AU - Shi, Yonghong
AU - Shen, Dinggang
N1 - Funding Information:
This work is supported by Science and Technology Commission of Shanghai Municipality of China (Grant number: 06dz22103).
PY - 2008
Y1 - 2008
N2 - We previously developed a deformable model for segmenting lung fields in serial chest radiographs by using both population-based and patient-specific shape statistics, and obtained higher accuracy compared to other methods. However, this method uses an ad hoc way to evenly partition the boundary of lung fields into some short segments, in order to capture the patient-specific shape statistics from a small number of samples by principal component analysis (PCA). This ad hoc partition can lead to a segment including points with different amounts of longitudinal deformations, thus rendering it difficult to capture principal variations from a small number of samples using PCA. In this paper, we propose a learning technique to adaptively partition the boundary of lung fields into short segments according to the longitudinal deformations learned for each boundary point. Therefore, all points in the same short segment own similar longitudinal deformations and thus small variations within all longitudinal samples of a patient, which enables effective capture of patient-specific shape statistics by PCA. Experimental results show the improved performance of the proposed method in segmenting the lung fields from serial chest radiographs.
AB - We previously developed a deformable model for segmenting lung fields in serial chest radiographs by using both population-based and patient-specific shape statistics, and obtained higher accuracy compared to other methods. However, this method uses an ad hoc way to evenly partition the boundary of lung fields into some short segments, in order to capture the patient-specific shape statistics from a small number of samples by principal component analysis (PCA). This ad hoc partition can lead to a segment including points with different amounts of longitudinal deformations, thus rendering it difficult to capture principal variations from a small number of samples using PCA. In this paper, we propose a learning technique to adaptively partition the boundary of lung fields into short segments according to the longitudinal deformations learned for each boundary point. Therefore, all points in the same short segment own similar longitudinal deformations and thus small variations within all longitudinal samples of a patient, which enables effective capture of patient-specific shape statistics by PCA. Experimental results show the improved performance of the proposed method in segmenting the lung fields from serial chest radiographs.
KW - Active shape model
KW - Hierarchical principal component analysis
KW - Scale space analysis
KW - Statistical model
UR - http://www.scopus.com/inward/record.url?scp=50249108488&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-79982-5_45
DO - 10.1007/978-3-540-79982-5_45
M3 - Conference contribution
AN - SCOPUS:50249108488
SN - 3540799818
SN - 9783540799818
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 413
EP - 420
BT - Medical Imaging and Augmented Reality - 4th International Workshop, Proceedings
T2 - 4th International Workshop on Medical Imaging and Augmented Reality, MIAR 2008
Y2 - 1 August 2008 through 2 August 2008
ER -