A Texton-Based Approach for the Classification of Lung Parenchyma in CT Images

摘要：

In this paper, a texton-based classification system based on raw pixel representation along with a support vector machine with radial basis function kernel is proposed for the classification of emphysema in computed tomography images of the lung. The proposed approach is tested on 168 annotated regions of interest consisting of normal tissue, centrilobular emphysema, and paraseptal emphysema. The results show the superiority of the proposed approach to common techniques in the literature including moments of the histogram of filter responses based on Gaussian derivatives. The performance of the proposed system, with an accuracy of 96.43％, also slightly improves over a recently proposed approach based on local binary patterns.

作者: Mehrdad J.Gangeh Lauge Soensen Saher B.Shaker Mohamed S.Kamel Marleen de Bruijne Marco Loog

作者单位: Department of Electrical and Computer Engineering, University of Waterloo, Canada Department of Computer Science, University of Copenhagen, Denmark Department of Respiratory Medicine, Gentofte University Hospital, Hellerup, Denmark Department of Computer Science, University of Copenhagen, Denmark Biomedical Imaging Group Rotterdam Pattern Recognition Laboratory, Delft University of Technology, The Netherlands

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 595–602

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）