中国科学院深圳先进技术研究院机构知识库(SIAT OpenIR): Lung segmentation based on customized active shape model from digital radiography chest images
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Lung segmentation based on customized active shape model from digital radiography chest images
Gang Wu; Xiaodong Zhang; Suhuai Luo; Qingmao Hu
2015
Source PublicationJournal of Medical Imaging and Health Informatics
Subtype期刊论文
AbstractIn this paper, a customized active shape model to extract lungs from radiography chest images was proposed and validated. Firstly, the average active shape model, gray-scale projection and affine registration were employed to attain the initial lung contours. Secondly, a new objective function with constraints of distance and edge was proposed to push the vertices of active shape model to the real lung edge, pull the vertices out of the stomach gas regions, and have a more balanced distance distribution of vertices. Finally, multi-resolution representation and optimization were employed to attain fast optimization. Experimental results on a public database of 247 images showed that the proposed algorithm could achieve an average accuracy of 94.7%, which is 4.4% better than the traditional active shape model and 2.7% better than the active shape model with local invariant features.
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Indexed BySCI
Language英语
Department医学图像与数字手术研究室
Document Type期刊论文
Identifierhttp://ir.siat.ac.cn/handle/172644/7083
Collection医工所
AffiliationJournal of Medical Imaging and Health Informatics
Recommended Citation
GB/T 7714
Gang Wu,Xiaodong Zhang,Suhuai Luo,et al. Lung segmentation based on customized active shape model from digital radiography chest images[J]. Journal of Medical Imaging and Health Informatics,2015.
APA Gang Wu,Xiaodong Zhang,Suhuai Luo,&Qingmao Hu.(2015).Lung segmentation based on customized active shape model from digital radiography chest images.Journal of Medical Imaging and Health Informatics.
MLA Gang Wu,et al."Lung segmentation based on customized active shape model from digital radiography chest images".Journal of Medical Imaging and Health Informatics (2015).
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