基于Snake模型的阿尔茨海默病早期检测脑MRI分割方法探讨  

Brain MRI segmentation method for early detection of Alzheimer disease based on the Snake model

作　　者：李钦华 熊宇晴 LI Qinhua;XIONG Yuqing(Information Engineering College,Jiangxi University of Technology,Nanchang,Jiangxi 330098,China)

机构地区：[1]江西科技学院信息工程学院,江西南昌330098

出　　处：《影像研究与医学应用》2025年第5期28-30,34,共4页Journal of Imaging Research and Medical Applications

基　　金：2022年度江西科技学院校级自然科学项目(23ZRYB02)。

摘　　要：目的:改进Snake模型以提高脑MRI图像分割的准确性和效率,增强阿尔茨海默病(AD)早期检测对复杂脑部结构变化的处理精度。方法:使用改进的Snake模型对ADNI数据库中的脑MRI图像进行分割,经过数据预处理和能量函数参数优化,并采用交叉验证评估分割性能。结果:改进后的Snake模型在处理复杂脑部结构变化时精度和稳定性更高。相比传统模型,优化算法在轮廓平均曲率和区域重叠度上表现更优,同时保持了合理的运行时间,适合临床实时应用。结论:改进的Snake模型显著提升了脑MRI图像分割的精度和效率,对AD早期检测和诊断具有较高的鲁棒性和应用价值。Objective To enhance the accuracy and efficiency of brain MRI image segmentation by improving the Snake model,thereby increasing the precision of early Alzheimer disease(AD)detection in complex brain structure changes.Methods Employed an improved Snake model to segment brain MRI images from the ADNI database,with data preprocessing and energy function parameter optimization applied.Cross-validation was employed to assess the segmentation performance.Results The improved Snake model demonstrated higher accuracy and stability in handling complex brain structure changes.Compared to the traditional model,the optimized algorithm showed superior performance in contour average curvature and region overlap metrics while maintaining a reasonable runtime,making it suitable for clinical real-time applications.Conclusion The improved Snake model significantly improves the accuracy and efficiency of brain MRI image segmentation,providing higher robustness and application value for early Alzheimer disease detection and diagnosis.

关 键 词：阿尔茨海默病 SNAKE模型 脑部磁共振 图像分割 磁共振成像 

分 类 号：R445.2[医药卫生—影像医学与核医学]