Hippocampal Automatic Recognition and 3D Segmentation Based on Active Appearance Model in Brain MR Images for Early Diagnosis ofAlzheimer's Disease

Abstract

目的:研究磁共振(MAgnETIC rESOnAnCE,Mr)脑图像中海马的自动分割方法及海马的形态学分析方法,为阿尔茨海默病(AlzHEIMEr'S dISEASE,Ad)的早期诊断提供依据。方法:对20例Ad患者和60名正常对照者行MrI T1 WI 3d容积扫描,建立海马的三维主动表观模型,并以此模型对每个个体脑部磁共振图像上的海马进行自动识别和三维分割,分别建立正常对照组和Ad组的海马统计形状模型,比较Ad组与正常对照组间海马形状的差异性。结果:海马三维分割方法与手动分割方法在海马体积测量上无统计学差别(P>0.05);Ad患者海马头部发生萎缩(P0.05).Hippocampal head atrophy was found in AD patients(P<0.05).Conclusions: Hippocampal three-dimensional segmentation and automatic identification method based on active appearance model in brain MR image is accurate and reliable;the feature of hippocampal head atrophy can be used as a basis for diagnosis of AD.哈尔滨医科大学研究生创新基金(HCXB2010019);国家自然科学基金(81071219