基于模糊贝叶斯网络的星形细胞瘤智能分级模型  

A Fuzzy Bayesian Network-base Intelligent Model for Astrocytoma Malignant Degree

作　　者：林春漪[1] 尹俊勋[1] 马丽红[1] 陈健宇[2] 王奎健[3] 

机构地区：[1]华南理工大学电子与信息学院,广东广州510640 [2]中山大学第二附属医院,广东广州510120 [3]中山大学生物医学工程系,广东广州510080

出　　处：《中国医学物理学杂志》2006年第3期174-177,193,共5页Chinese Journal of Medical Physics

基　　金：中山大学985工程资助项目;国家自然科学基金项目(60472063);广东省自然科学基金项目(04020074/CN200402)

摘　　要：本研究提出一种新的融合影像低层视觉特征和语义的模糊贝叶斯网络模型,使用了高斯混合模型(GMM)对连续的视觉特征模糊化处理,解决了传统贝叶斯网络不能操作连续输入的问题,更合理地表达了具有模糊性、不确定性的专业领域的结构性知识。为了验证它的有效性,将它应用于星形细胞瘤恶性程度的分级,建立了一个概率模型,实验结果得出83.33%的正确识别率。该模型为星形细胞瘤恶性程度预测提供了新的定量而客观的辅助手段。This study proposes a form of fuzzy Bayesian networks fusing continuous low-level image features and high-level semantics, which uses Gaussian mixture models （GMM） to make a fuzzy procedure. This particular procedure will transform continuous variables into discrete ones,when dealing with continuous inputs with probabilistic and uncertain nature,so that it can settle continuous inputs that discrete Bayesian networks can＇t handle. Moreover,it describes structure knowledge in fuzzy and uncertain domain more reasonably. To demonstrate the validity of this method,we applied it to classification of astrocytoma malignant degree, and built a probabilistic model to predict astrocytoma malignant level. An accuracy of 83.33% is achieved out of testing 60 samples （30 benign and 30 malignant astrocytoma）. It provides a novel objective method to quantitatively assess the astrocytoma malignant level that can be used to assist doctors to diagnose the tumor.

关 键 词：模糊贝叶斯网络 星形细胞瘤 高斯混合模型 机器学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]