Classification of Medical Brain Images

Since brain tumors endanger people‘s living quality and even their lives, the accuracy of classification becomes more important. Conventional classifying techniques are used to deal with thosedatasets with characters and numbers. It is difficult, however, to apply them to datasets that include brain images and medical history (alphanumeric data), especially to guarantee the accuracy.For these datasets, this paper combines the knowledge of medical field and improves the traditional decision tree. The new classification algorithm with the direction of the medical knowledge not only adds the interaction with the doctors, but also enhances the quality of classification. The algorithm has been used on real brain CT images and a precious rule has been gained from the experiments. This oaoer shows that the ahzorithm works well for real CT data.     

领域知识驱动的医学图像关联模式挖掘算法

根据医学图像领域的相关知识,提出了关键像素区域ROI的概念及其特征属性针对ROI聚类后的图像数据进行挖掘,提出了一种基于对FP-GROWTH算法改进的Diff-FP-GROWTH算法。最后,给出了该算法的实例分析和该算法的实际研究意义。     

基于多模态医学图像的Alzheimer病分类方法

多模态医学影像信息已经在计算机辅助检测和诊断中被广泛地应用。在对Alzheimer病(Alzheimer's disease, AD)的分类与诊断中,结合多个模态医学影像的特征信息能够更加准确且全面地对同一AD主题进行分类与诊断。该文提出了一种基于卷积神经网络的模型结构,分别对AD病患的磁共振图像(MRI)和正电子发射型计算机断层显像(PET)图像进行3D卷积操作来提取各自模态的特征信息,并应用模型融合方法对模态特征信息加以融合,从而得到包含更加丰富的多模态特征信息。最后用全连接神经网络将上述提取的多模态特征信息进行分类预测。通过在AD神经影像学倡议(Alzheimer's disease neuroimaging initiative, ADNI)公开数据集上的实验结果表明:该文所提出的模型在准确率(accuracy, ACC)和曲线下面积(area under the curve, AUC)的性能评价中都取得了更加优越的结果。     

高维空间范围查询并行算法研究

采用并行技术提出一种高维空间范围查询并行算法,该算法将d维空间范围查询转换成d个从节点机上的一维空间范围查询,d个从节点机可以并行执行查询操作,主节点机只需要求得d个从节点机返回结果的交集,因此大大减少了查询时间.实验结果表明在高维空间中算法的查询效率优于上述算法.