Near‐optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra
Near‐optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra
复制标题
用于特征空间缩减的近最优区域选择：用于分类 MR 谱的新颖预处理方法
DOI：
10.1002/(sici)1099-1492(199806/08)11:4/5
复制
发表时间：
1998
影响因子：
2.9
通讯作者：
R. Somorjai
中科院分区：
文献类型：
--
作者：
A. Nikulin;B. Dolenko;T. Bezabeh;R. Somorjai
我们介绍了一种专门为生物医学来源的磁共振谱进行预处理而设计的全局特征提取方法。这种预处理对于准确和可靠地对光谱中显示的疾病或疾病阶段进行分类是必不可少的。新方法是由遗传算法指导的。它与我们的标准前向选择算法的增强版本进行了比较。两者都在寻找和选择最佳谱区。这些亚区必须保留光谱信息，从而有助于最终识别疾病存在和发展的生物化学。两个生物医学实例证明了该方法的有效性：脑组织活检中脑膜瘤和星形细胞瘤的区分，以及大肠活检分为正常和肿瘤两类。两种方法的分类正确率均在97%以上。©1998 John Wiley&Sons，Ltd.
We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is genetic algorithm‐guided. It is compared with our enhanced version of the standard forward selection algorithm. Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. The power of the methods is demonstrated on two biomedical examples: the discrimination between meningioma and astrocytoma in brain tissue biopsies, and the classification of colorectal biopsies into normal and tumour classes. Both preprocessing methods lead to classification accuracies over 97% for the two examples. © 1998 John Wiley & Sons, Ltd.