改进的分段主成分分析算法及其在前列腺分割中的应用Improved modular principal component analysis algorithm and its application in prostate image segmentation
宋建萍,石勇涛
摘要(Abstract)：
主成分分析(PCA)作为形状建模中的经典算法,在训练阶段考虑训练样本的整体信息,而忽略了样本的局部细节信息。分段主成分分析(MPCA)针对PCA的不足改进了算法,在人脸识别应用中获得了比传统PCA更好的识别效果。但在MPCA中样本一般都被划分为同样大小的子样本块,没有考虑到实际的样本局部动态变化信息。这里根据初始样本的方差信息对MPCA算法进行改进,将样本划分成尺寸大小不一的多类样本(分段样本),然后分别对分段样本做主成分分析,得到原始样本的分段PCA模型。将该模型应用于前列腺超声图像分割实验,结果表明其分割效果优于传统的PCA算法和MPCA算法。
关键词(KeyWords)： 医学超声图像分割;先验形状;分段样本;分段主成分分析;前列腺图像分割;信息提取
基金项目(Foundation): 国家自然科学基金资助项目(U1401252)~~
作者(Author): 宋建萍,石勇涛
DOI: 10.16652/j.issn.1004-373x.2018.13.014
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