@inproceedings{eb49aeba70b54856a3e49fb0708d94a3,
title = "Automatic liver segmentation in CT images based on support vector machine",
abstract = "Accurate and fully automated segmentation of liver parenchyma in medical images is necessary prerequisites for a variety of clinical and research applications, such as constructing three dimension anatomical model. In this paper, an automatic liver segmentation method based on Support Vector Machines (SVM) has been proposed. Segmentation is started by wavelet transform for image feature extraction. Subsequently, SVM is applied on the feature vectors for training and testing to realize pixel classification. Finally, region-growing is used to refine the result of SVM. Experiments have been conducted on different training-test partitions of the CT image datasets. Compared to manual segmentation provided by medical experts, our experimental results demonstrated the effectiveness of the proposed method.",
keywords = "Liver segmentation, machine learning, support vector machine",
author = "Jie Lu and Defeng Wang and Lin Shi and Heng, \{Pheng Ann\}",
year = "2012",
doi = "10.1109/BHI.2012.6211581",
language = "英语",
isbn = "9781457721779",
series = "Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics: Global Grand Challenge of Health Informatics, BHI 2012",
pages = "333--336",
booktitle = "Proceedings - IEEE-EMBS International Conference on Biomedical and Health Informatics",
note = "IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health Engineering ; Conference date: 02-01-2012 Through 07-01-2012",
}