Brain Morphometry by Probabilistic Latent Semantic Analysis

摘要：

The paper proposes a new shape morphometry approach that combines advanced classification techniques with geometric features to identify morphological abnormalities on the brain surface. Our aim is to improve the classification accuracy in distinguishing between normal subjects and schizophrenic patients. The approach is inspired by natural language processing. Local brain surface geometric patterns are quantized to visual words, and their co-occurrences are encoded as visual topic. To do this, a generative model, the probabilistic Latent Semantic Analysis is learned from quantized shape descriptors (visual words). Finally, we extract from the learned models a generative score, that is used as input of a Support Vector Machine (SVM), defining an hybrid generative/discriminative classification algorithm. An exhaustive experimental section is proposed on a dataset consisting of MRI scans from 64 patients and 60 control subjects. Promising results are reporting by observing accuracies up to 86.13％.

作者: U.Castellani A.Perina V.Murino M.Bellani G.Rambaldelli M.Tansella P.Brambilla

作者单位: VIPS lab, University of Verona, Italy VIPS lab, University of Verona, ItalyIstituto Italiano di Tecnologia (IIT), Italy Istituto Italiano di Tecnologia (IIT), Italy Department of Medicine and Public Health, University of Verona, Italy Department of Medicine and Public Health, University of Verona, Italy ICBN Center, University of Udi

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: 177-184

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）