TY - JOUR
T1 - Organ location determination and contour sparse representation for multiorgan segmentation
AU - Li, Siqi
AU - Jiang, Huiyan
AU - Yao, Yu Dong
AU - Yang, Benqiang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2018/5
Y1 - 2018/5
N2 - Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed. A sparse optimization model is developed for refining the contour feature points. Experimentations with 153 CT images demonstrate the performance advantages of OLD-CSR as compared with related work.
AB - Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed. A sparse optimization model is developed for refining the contour feature points. Experimentations with 153 CT images demonstrate the performance advantages of OLD-CSR as compared with related work.
KW - Energy function
KW - extreme learning machine
KW - image segmentation
KW - location determination
KW - sparse optimization
UR - http://www.scopus.com/inward/record.url?scp=85048734452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048734452&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2017.2705037
DO - 10.1109/JBHI.2017.2705037
M3 - Article
C2 - 28534802
AN - SCOPUS:85048734452
SN - 2168-2194
VL - 22
SP - 852
EP - 861
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 3
ER -