Multi-feature analysis and classification of human chromosome images using centromere segmentation algorithms

Classification of homologous human chromosomes is essential to advanced studies of cancer genetics. This paper describes novel segmentation and classification algorithms to extract multiple features, from microscopy images of chromosomes, for classification purposes. Multicolour images of metaphase chromosomes prepared by applying PNA probes are used for this purpose. Centromeres are segmented using an iterative fuzzy algorithm as well as a gradient method. Moreover, telomere length measurements are performed on chromosome images and normalized for the image database. Multiple intensity features are calculated as a result of the developed algorithms. Heteromorphic chromosomes (such as 16 and 22) are then successfully classified into their parental homologues, based on the calculated multiple features, and used to verify the developed methods