Automatic context-sensitive karyotyping of human chromosomes based on elliptically symmetric statistical distributions

摘要

We introduce a statistical model of a metaphase cell consisting of independent chromosomes with elliptically symmetric feature vectors. From this model we derive the ML-classifier for classification in the 24 chromosomal classes, taking into account the correct number of chromosomes in each class. Experimental results show that error rates of the best of these classifiers are less than 2% with respect to chromosomes if applied to the large Copenhagen data set Cpr. Simulation studies suggest that there should be even more information contained in the features of this data set.