摘要
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。
The whole information of the training samples is considered in the training stage,but the local detailed information is ignored in principal component analysis（PCA）,which is regarded as a classical algorithm of shape modeling. The modular PCA（MPCA）can make up for the deficiency of PCA,and obtain better recognition results than the conventional PCA in the application of face recognition,but in MPCA,the training example is generally divided into subsamples with the same size,and the local dynamic variation information of the practical sample isn ′ t considered. On the basis of the variance information of the initial information,the MPCA algorithm is improved,and the sample is divided into segmented samples with different size. The PCA is performed for the segmented samples to get the MPCA model of the initial sample. The model was applied to the segmentation experiment of prostate ultrasonic image. The experimental results show that the proposed algorithm is superior to the classical PCA and modular PCA algorithms.
作者
宋建萍
石勇涛
SONG Jianping;SHI Yongtao(College of Science and Technology,China Three Gorges University,Yiehang 443002,China;College of Computer and Information Technology,China Three Gorges University,Yichang 443002,China)
出处
《现代电子技术》
北大核心
2018年第13期61-64,共4页
Modern Electronics Technique
基金
国家自然科学基金资助项目(U1401252)~~
关键词
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
medical ultrasonic in]age segmentation
prior shape
segmented example
modular PCA
prostate in]agesegmentation
information exlxacfion
