TY - GEN
T1 - Segmenting lung fields in serial chest radiographs using both population and patient-specific shape statistics
AU - Shi, Yonghong
AU - Qi, Feihu
AU - Xue, Zhong
AU - Ito, Kyoko
AU - Matsuo, Hidenori
AU - Shen, Dinggang
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - 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-specified shape statistics. Initially, population-based shape statistics takes most of the rules when the number of serial images is small; gradually, patient-specific shape statistics takes more rules 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.
AB - 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-specified shape statistics. Initially, population-based shape statistics takes most of the rules when the number of serial images is small; gradually, patient-specific shape statistics takes more rules 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.
UR - http://www.scopus.com/inward/record.url?scp=33750250277&partnerID=8YFLogxK
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U2 - 10.1007/11866565_11
DO - 10.1007/11866565_11
M3 - Conference contribution
C2 - 17354877
AN - SCOPUS:33750250277
SN - 3540447075
SN - 9783540447078
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 83
EP - 91
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006 - 9th International Conference, Proceedings
PB - Springer Verlag
T2 - 9th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2006
Y2 - 1 October 2006 through 6 October 2006
ER -