机构:[1]School of biomedical engineering, Capital Medical University, Beijing China.[2]School of biomedical engineering, Capital Medical University, Beijing China.[3]Xuan Wu Hospital affiliated to Capital Medical University首都医科大学宣武医院
In this paper, we proposed a mathod for lumbar vertebra CT image segmentation based on the contourlet transform and artificial neural networks(ANNs). The proposed method consists of three portions. In the first part, contourlet transform is used to decompose the CT image to obtain the contourlet coefficients. In the second part, the self-organizing competitive artificial neural network is employed to optimize and extract the low frequency coefficients coefficients of contourlet transformation, reduce the number of coefficients greatly. The last part, the optimized coefficients are inverse contourlet transformed with the original coefficients,the segmented image is reconstructed. The experimental results show the accuracy of human lumbar vertebra CT image segmentation based on the proposed method is encouraged.
基金:
the National Natural Science Foundation of China(No.30670576)
Scientific Research Key Program of Beijing Municipal Commission of Education(No.kz200810025011)
Medicine and Clinical Cooperative Research Program of Capital Medical University( Grant No. 10JL24)
语种:
外文
被引次数:
WOS:
第一作者:
第一作者机构:[1]School of biomedical engineering, Capital Medical University, Beijing China.
通讯作者:
通讯机构:[2]School of biomedical engineering, Capital Medical University, Beijing China.
推荐引用方式(GB/T 7714):
Junmin Deng,Haiyun Li,Hao Wu.An approach to lumbar vertebra CT image segmentation using contourlet transform and ANNs[J].AUTOMATION EQUIPMENT AND SYSTEMS, PTS 1-4.2012,468-471:doi:10.4028/www.scientific.net/AMR.468-471.613.
APA:
Junmin Deng,Haiyun Li&Hao Wu.(2012).An approach to lumbar vertebra CT image segmentation using contourlet transform and ANNs.AUTOMATION EQUIPMENT AND SYSTEMS, PTS 1-4,468-471,
MLA:
Junmin Deng,et al."An approach to lumbar vertebra CT image segmentation using contourlet transform and ANNs".AUTOMATION EQUIPMENT AND SYSTEMS, PTS 1-4 468-471.(2012)