Homologue matching using the Choquet integral.

Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes, 1-22, and X chromosome for females. For optimizing automated human chromosome image analysis, many existing techniques assume cell normalcy. With many genetic abnormalities directly linked to structural and numerical aberrations of chromosomes within the metaphase spread, the two chromosome per class assumption may not be appropriate for anomaly analysis. At the University of Missouri, a data-driven homologue matching approach has been developed to identify all normal chromosomes within a metaphase spread from a selected class. Chromosome assignment to a specific class is initially based on neural networks, followed by banding pattern and centromeric index criteria checking, and concluding with homologue matching utilizing a density profile-based classifier, a shape profile-based classifier, and a binary band profile-based classifier. Based on preliminary results for the profile-based classifiers assigning chromosome 17, the Choquet integral is presented as an extension to the homologue matching approach. Experimental results are presented comparing the extended homologue matching approach to the transportation algorithm for identifying chromosome 21 within normal metaphase spreads.