多对比度复合图像中细胞的多类别检测。
Multiclass detection of cells in multicontrast composite images.
机构信息
Mechanical Engineering Department, Columbia University, New York, NY 10027, USA.
出版信息
Comput Biol Med. 2010 Feb;40(2):168-78. doi: 10.1016/j.compbiomed.2009.11.013. Epub 2009 Dec 22.
In this paper, we describe a framework for multiclass cell detection in composite images consisting of images obtained with three different contrast methods for transmitted light illumination (referred to as multicontrast composite images). Compared to previous multiclass cell detection results [1], the use of multicontrast composite images was found to improve the detection accuracy by introducing more discriminatory information into the system. Preprocessing multicontrast composite images with Kernel PCA was found to be superior to traditional linear PCA preprocessing, especially in difficult classification scenarios where high-order nonlinear correlations are expected to be important. Systematic study of our approach under different overlap conditions suggests that it possesses sufficient speed and accuracy for use in some practical systems.
在本文中，我们描述了一个用于复合图像中多类细胞检测的框架，该复合图像由三种不同的透射光照明对比度方法获得的图像组成（称为多对比度复合图像）。与之前的多类细胞检测结果[1]相比，使用多对比度复合图像通过向系统中引入更多的鉴别信息，被发现可以提高检测精度。使用核主成分分析（Kernel PCA）预处理多对比度复合图像，被发现优于传统的线性主成分分析（PCA）预处理，尤其是在预计高阶非线性相关性很重要的困难分类场景中。在不同重叠条件下对我们的方法进行的系统研究表明，它具有足够的速度和准确性，可用于一些实际系统中。
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