Share Email Print
cover

Proceedings Paper

Liver recognition based on statistical shape model in CT images
Author(s): Dehui Xiang; Xueqing Jiang; Fei Shi; Weifang Zhu; Xinjian Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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%.

Paper Details

Date Published: 29 March 2016
PDF: 6 pages
Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97881O (29 March 2016); doi: 10.1117/12.2216071
Show Author Affiliations
Dehui Xiang, Soochow Univ. (China)
Xueqing Jiang, Soochow Univ. (China)
Fei Shi, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Xinjian Chen, Soochow Univ. (China)


Published in SPIE Proceedings Vol. 9788:
Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

© SPIE. Terms of Use
Back to Top