WAVELET BASED AUTOMATED CHROMOSOME IDENTIFICATION

自20世纪80年代中期推出以来，商业自动化 核型分析系统已经得到广泛使用。商业 工具已经改进到这样的程度， 操作员校正染色体所需的时间 分类错误。染色体分类法的改进 准确性将大大增加这些工具的价值， 实验室该项目的目标是开发改进的染色体 测量和分类技术来烘烤自动化的 人类和哺乳动物细胞的核型分析显著更快， 准确，从而提高这些仪器在临床中的实用性 诊断以及遗传学和癌症研究。目前，染色体 在商业系统中最广泛使用的分类技术是基于 加权密度分布（WDDs）[11]。使用的特征是内部的 产品之间的带状配置文件和六个化学定义的WDD。 虽然WDD已被证明优于其他两个权重 已经测试过的函数集（高斯函数和正弦函数）， 有理由相信它们对于染色体分类准确性是最佳的。 根据小波理论的最新发展，我们现在能够 设计数百种具有特定性质的新线性变换。 使用参数优化算法，我们可以测试它们的基函数 染色体分类的准确性。最近发展的小波 转换理论现在允许对染色体进行更直接的研究 分类问题，有可能大大改善 通过使用更适合的加权函数， WDD在第一阶段，我们将评估使用小波变换衍生 染色体自动分类中的特征。我们将使用一种基因 算法来评估大量的基于小波的功能，使用 用于培训和测试的大型已发布数据集。如果基于小波 功能可以影响改进，该仪器将产生更多 准确的核型，显著提高其吞吐率。在 第二阶段，证明最有效的基于小波的特征将是 将其并入PSI PowerGene自动核型分析仪中。我们 将在产前、产后和癌症样本上测试该系统， 羊水、骨髓和外周血以及哺乳动物细胞。 当完全开发后，新技术将被集成到PSI的 现有的细胞遗传学自动化产品线。这将导致 在自动化方面更有效的商业工具， 核型分析 拟定商业应用： 一旦新技术被开发出来，并有资格用于日常工作， 应用程序，他们将被纳入PSI的PowerGene产品线 细胞遗传学自动化设备，无论是在新的系统销售，并作为一个 升级到细胞遗传学实验室已经使用的现有系统， 使这项技术迅速商业化。

Since their introduction in the mid-198Os, commercial automated karyotyping systems have come into widespread usage. Commercial instruments have improved to the point where the major factor limiting throughput the time required for operator correction of chromosome classification errors. An improvement in chromosome classification accuracy would significantly increase the value of these instruments in the laboratory. The goal of this project is to develop improved chromosome measurement and classification techniques to bake the automated karyotyping of human and mammalian cells significantly faster and more accurate, thereby enhancing the utility of these instruments in clinical diagnosis and in genetics and cancer research. Currently, the chromosome classification technique most widely used in commercial systems is based on weighted density distributions (WDDs [11]). The features used are inner products between the banding profile and six heuristically-defined WDDs. While WDDs have been shown to outperform the only two other weighting function sets that have been tested (Gaussians and sinusoids), there is no reason to believe they are optimal for chromosome classification accuracy. In light of recent developments in wavelet theory we are now able to design hundreds of new linear transformations having specified properties. Using parameter optimization algorithms we an test their basis functions for chromosome classification accuracy. Recently developed wavelet transform theory now permits a much more directed study of the chromosome classification problem, with the possibility of significant improvement in classification accuracy by using better-suited weighting functions than the WDDs. In phase 1 we will evaluate the use of wavelet-transform-derived features in automatic chromosome classification. We will use a genetic algorithm to evaluate a large number of wavelet-based features using large, published data sets for training and testing. If wavelet-based features can effect an improvement, this instrument will produce more accurate karyotypes, significantly increasing its throughput rate. In Phase 2 the wavelet-based features that prove the most effective will be incorporated into a PSI PowerGene automated karyotyping instrument. We will test the system on prenatal, postnatal and cancer specimens from amniotic fluid, bone marrow and peripheral blood, and on mammalian cells. When fully developed, the new technology will be integrated into PSI's existing line of cytogenetics automation products. This will result in commercial instruments that are far more effective in automated karyotyping. PROPOSED COMMERCIAL APPLICATION: As soon as the new techniques are developed and qualified for routine application, they will be incorporated into PSI's PowerGene product line of cytogenetics automation equipment, both in new systems sold and as an upgrade to existing systems already in use in cytogenetics labs, thus commercializing the technology quickly.