| Summary: | The thesis considers two topics in the automation of cytogenetics and cytology: the automated allocation of human chromosomes to the twenty-four classes which humans possess; and the detection of abnormal cervical smear specimens. For chromosome allocation, the following work is presented and evaluated on a number of data sets derived from chromosome preparations of different quality:1. Three new procedures for modelling between-cell variation.2. Six ways of combining class information on variability in multivariate Normal discrimination.3. Covariance selection models for individual chromosome classes and an assumed common covariance structure for a number of classes.4. Some two-stage procedures for the calculation of discriminant scores in multivariate Normal discrimination.5. The application of some non-parametric and semi-parametric methods.6. The modelling of band-transition sequence probabilities. For the detection of abnormal cervical smear specimens, the use of a consensus probability of a specimen being abnormal, derived from a number of cytologists' assessments, is considered. The sequential use of multiple regression equations to try to predict the logit transformations of these consensus probabilities is described. Finally, the sequential use of features in multivariate discrimination is considered mainly for the case of two known multivariate Normal populations with equal covariance matrices.
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