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    • 14. 发明申请
    • METHODS, DATA STRUCTURES, AND SYSTEMS FOR CLASSIFYING MICROPARTICLES
    • 方法,数据结构和分类微阵列的系统
    • WO2007016517A1
    • 2007-02-08
    • PCT/US2006/029806
    • 2006-08-01
    • LUMINEX CORPORATIONCALVIN, Edward, A.ROTH, Wayne, D.
    • CALVIN, Edward, A.ROTH, Wayne, D.
    • G06F19/00G01N15/14
    • G01N15/1456G01N2015/1477G01N2015/1488G06F19/707
    • Methods, data structures, and systems for classifying particles are provided. In particular, the methods and systems are configured to acquire a first set of data corresponding to measurable parameters of a microparticle and identify a location of a look-up table to which the first set of data corresponds, wherein the look-up table is framed by values associated with at least one of the measurable parameters. Furthermore, the methods and systems are configured to determine whether the first set of data fits one or more predefined algorithms respectively indicative of a different microparticle classification associated with the identified location of the look-up table. The methods and systems are further configured to classifying the microparticle within at least one predefined categorization based upon the determination of whether the first set of data fits the one or more predefined algorithms.
    • 提供了分类粒子的方法,数据结构和系统。 特别地,方法和系统被配置为获取对应于微粒的可测量参数的第一组数据,并且识别第一组数据对应的查找表的位置,其中查找表被框架 通过与至少一个可测量参数相关联的值。 此外,方法和系统被配置为确定第一组数据是否适合分别指示与查找表的所识别的位置相关联的不同微粒分类的一个或多个预定义算法。 所述方法和系统还被配置为基于确定所述第一组数据是否符合所述一个或多个预定义算法来确定所述至少一个预定义分类中的所述微粒。
    • 16. 发明申请
    • FLUID SAMPLE ANALYSIS USING CLASS WEIGHTS
    • 使用等级重量的流体样品分析
    • WO2004113865A2
    • 2004-12-29
    • PCT/US2004/016158
    • 2004-05-24
    • INTERNATIONAL REMOTE IMAGING SYSTEMS, INC.CHAPOULAUD, EricKASDAN, Harvey, L.
    • CHAPOULAUD, EricKASDAN, Harvey, L.
    • G01N
    • G06K9/00127G01N15/1429G01N15/1463G01N2015/1465G01N2015/1486G01N2015/1488
    • Most automatic particle classification methods produce errors. The invention provides a method for improving the accuracy of particle classification while shortening the amount of manual review time required from the operator. The method uses class weights, which are statistically-derived correction factors that accounts for frequency of classification errors. A first class weight and a second class weight are assigned to the first class and the second class, respectively. The number of particles in each of the first and the second classes is multiplied by the first class weight and the second class weight, respectively, to generate a corrected number of particles in each of the classes. If particles are reclassified, the class weights are recalculated in response to the reclassification. The method is usable with a complete classification where all the particles in a sample are classified, or a selective classification of a subset of the particles in the sample.
    • 大多数自动粒子分类方法产生错误。 本发明提供了一种提高粒子分级精度的方法,同时缩短了操作者所需的手动审查时间。 该方法使用类权重,这是统计学派生的校正因子,考虑到分类错误的频率。 第一类和第二类的权重分别分配给第一类和第二类。 第一类和第二类中的每一个中的粒子数分别乘以第一等级权重和第二类权重,以在每个类中生成校正的粒子数。 如果粒子重新分类,则根据重新分类重新计算类别权重。 该方法可用于将样品中所有颗粒分类的完整分类,或样品中颗粒子集的选择性分类。