CASIA OpenIR  > 09年以前成果
Regional homogeneity and anatomical parcellation for fMRI image classification: application to schizophrenia and normal controls
Feng Shi; Yong Liu; Tianzi Jiang; Wanlin Zhu; Jiefeng Jiang; Haihong Liu; Zhening Liu; Jiang Tianzi
2007
Conference NameMICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Source PublicationMed Image Comput Comput Assist Interv. 2007
Conference Date2007
Conference PlaceUSA
AbstractThis paper presents a discriminative model of multivariate pattern classification, based on functional magnetic resonance imaging (fMRI) and anatomical template. As a measure of brain function, Regional homogeneity (ReHo) is calculated voxel by voxel, and then a widely used anatomical template is applied on ReHo map to parcelate it into 116 brain regions. The mean and standard deviation of ReHo values in each region are extracted as features. Pseudo-Fisher Linear Discriminant Analysis (PFLDA) is performed for training samples to generate discriminative model. Classification experiments have been carried out in 48 schizophrenia patients and 35 normal controls. Under a full leave-one-out (LOO) cross-validation, correct prediction rate of 80% is achieved. Anatomical parcellation process is proved useful to improve classification rate by a control experiment. The discriminative model shows its ability to reveal abnormal brain functional activities and identify people with schizophrenia.
KeywordPattern Recognitio
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13197
Collection09年以前成果
Corresponding AuthorJiang Tianzi
Recommended Citation
GB/T 7714
Feng Shi,Yong Liu,Tianzi Jiang,et al. Regional homogeneity and anatomical parcellation for fMRI image classification: application to schizophrenia and normal controls[C],2007.
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