AUTOMATIC SEGMENTATION OF HIPPOCAMPAL SUBFIELDS BASED ON MULTI-ATLAS IMAGE SEGMENTATION TECHNIQUES  

作　　者：Shi Yonggang Zhang Xueping Liu Zhiwen 

机构地区：[1]Department of Electronic Engineering,School of Information and Electronics,Beijing Institute of Technology

出　　处：《Journal of Electronics(China)》2014年第2期121-128,共8页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China(Nos.60971133,61271112)

摘　　要：The volume of hippocampal subfields is closely related with early diagnosis of Alzheimer's disease.Due to the anatomical complexity of hippocampal subfields,automatic segmentation merely on the content of MR images is extremely difficult.We presented a method which combines multi-atlas image segmentation with extreme learning machine based bias detection and correction technique to achieve a fully automatic segmentation of hippocampal subfields.Symmetric diffeomorphic registration driven by symmetric mutual information energy was implemented in atlas registration,which allows multi-modal image registration and accelerates execution time.An exponential function based label fusion strategy was proposed for the normalized similarity measure case in segmentation combination,which yields better combination accuracy.The test results show that this method is effective,especially for the larger subfields with an overlap of more than 80%,which is competitive with the current methods and is of potential clinical significance.The volume of hippocampal subfields is closely related with early diagnosis of Alzheimer＇s disease. Due to the anatomical complexity of hippocampal subfields, automatic segmentation merely on the content of MR images is extremely difficult. We presented a method which combines multi-atlas image segmentation with extreme learning machine based bias detection and correction technique to achieve a fully automatic segmentation of hippocampal subfields. Symmetric diffeomorphic registration driven by symmetric mutual information energy was implemented in atlas registration, which allows multi-modal image registration and accelerates execution time. An exponential function based label fusion strategy was proposed for the normalized similarity measure case in segmentation combination, which yields better combination accuracy. The test results show that this method is effective, especially for the larger subfields with an overlap of more than 80%, which is competitive with the current methods and is of potential clinical significance.

关 键 词：Hippocampal subfields Image segmentation Symmetric diffeomorphism Mutual information Label fusion Extreme Learning Machine(ELM) 

分 类 号：R319[医药卫生—基础医学] R445