Automatic landmarking of 2D medical shapes using the growing neural gas network

被引:0
作者
Angelopoulou, A [1 ]
Psarrou, A
Rodríguez, JG
Revett, K
机构
[1] Univ Westminster, Harrow Sch Comp Sci, Harrow HA1 3TP, Middx, England
[2] Univ Alicante, Dept Tecnol Informat & Computac, E-03080 Alicante, Spain
来源
COMPUTER VISION FOR BIOMEDICAL IMAGE APPLICATIONS, PROCEEDINGS | 2005年 / 3765卷
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
MR Imaging techniques provide a non-invasive and accurate method for determining the ultra-structural features of human anatomy. In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. Our approach is based on an automated landmark extraction algorithm which automatically selects points along the contour of the ventricles from a series of 2D MRI brain images. Automated landmark extraction is accomplished through the use of the self-organising network the growing neural gas (CNG) which is able to topographically map the low dimension of the network to the high dimension of the manifold of the contour without requiring a priori knowledge of the structure of the input space. The GNG method is compared to other self-organising networks such as Kohonen and Neural Gas (NG) maps and an error metric is applied to quantify the performance of our algorithm compared to the other two.
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页码:210 / 219
页数:10
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