29 March 2016 Liver recognition based on statistical shape model in CT images
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Abstract
In this paper, an automatic method is proposed to recognize the liver on clinical 3D CT images. The proposed method effectively use statistical shape model of the liver. Our approach consist of three main parts: (1) model training, in which shape variability is detected using principal component analysis from the manual annotation; (2) model localization, in which a fast Euclidean distance transformation based method is able to localize the liver in CT images; (3) liver recognition, the initial mesh is locally and iteratively adapted to the liver boundary, which is constrained with the trained shape model. We validate our algorithm on a dataset which consists of 20 3D CT images obtained from different patients. The average ARVD was 8.99%, the average ASSD was 2.69mm, the average RMSD was 4.92mm, the average MSD was 28.841mm, and the average MSD was 13.31%.
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Dehui Xiang, Dehui Xiang, Xueqing Jiang, Xueqing Jiang, Fei Shi, Fei Shi, Weifang Zhu, Weifang Zhu, Xinjian Chen, Xinjian Chen, "Liver recognition based on statistical shape model in CT images", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881O (29 March 2016); doi: 10.1117/12.2216071; https://doi.org/10.1117/12.2216071
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