WAVELET BASED AUTOMATED CHROMOSOME IDENTIFICATION

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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.