TY - GEN
T1 - Segmentation of hepatic tumor from abdominal CT data using an improved support vector machine framework
AU - Zhou, Jiayin
AU - Huang, Weimin
AU - Xiong, Wei
AU - Chen, Wenyu
AU - Venkatesh, Sudhakar K.
PY - 2013
Y1 - 2013
N2 - An improved support vector machine (SVM) framework has been developed to segment hepatic tumor from CT data. By this method, the one-class SVM (OSVM) and two-class SVM (TSVM) are connected seamlessly by a boosting tool, to tackle the tumor segmentation via both offline and online learning. An initial tumor region was first pre-segmented by an OSVM classifier. Then the boosting tool was employed to automatically generate the negative (non-tumor) samples, according to certain criteria. The pre-segmented initial tumor region and the non-tumor samples generated were used to train a TSVM) classifier. By the trained TSVM classifier, the final tumor lesion was segmented out. Tested on 16 sets of CT abdominal scans, quantitative results suggested that the developed method achieved significantly higher segmentation accuracy than the OSVM and TSVM classifiers.
AB - An improved support vector machine (SVM) framework has been developed to segment hepatic tumor from CT data. By this method, the one-class SVM (OSVM) and two-class SVM (TSVM) are connected seamlessly by a boosting tool, to tackle the tumor segmentation via both offline and online learning. An initial tumor region was first pre-segmented by an OSVM classifier. Then the boosting tool was employed to automatically generate the negative (non-tumor) samples, according to certain criteria. The pre-segmented initial tumor region and the non-tumor samples generated were used to train a TSVM) classifier. By the trained TSVM classifier, the final tumor lesion was segmented out. Tested on 16 sets of CT abdominal scans, quantitative results suggested that the developed method achieved significantly higher segmentation accuracy than the OSVM and TSVM classifiers.
UR - http://www.scopus.com/inward/record.url?scp=84886532620&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2013.6610258
DO - 10.1109/EMBC.2013.6610258
M3 - Conference contribution
C2 - 24110445
AN - SCOPUS:84886532620
SN - 9781457702167
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 3347
EP - 3350
BT - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
T2 - 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013
Y2 - 3 July 2013 through 7 July 2013
ER -