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Maximum likelihood techniques for joint segmentation-classification of multi-spectral chromosome images
W.C. Schwartzkopf, A.C. Bovik and B.L. Evans
IEEE Transactions on Medical Imaging
Keywords: Chromosomes, image segmentation, karyotyping, object recognition, partial occlusion
Abstract
Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed wherein each class of chromosomes binds with a different combination of fluorophores. This results in a multispectral image, where each class of chromosomes has distinct spectral components. In this paper, we develop new methods for automatic chromosome identification by exploiting the multispectral information in M-FISH chromosome images and by jointly performing chromosome segmentation and classification. We 1) develop a maximum-likelihood hypothesis test that uses multispectral information, together with conventional criteria, to select the best segmentation possibility; 2) use this likelihood function to combine chromosome segmentation and classification into a robust chromosome identification system; and 3) show that the proposed likelihood function can also be used as a reliable indicator of errors in segmentation, errors in classification, and chromosome anomalies, which can be indicators of radiation damage, cancer, and a wide variety of inherited diseases. We show that the proposed multispectral joint segmentation-classification method outperforms past grayscale segmentation methods when decomposing touching chromosomes. We also show that it outperforms past M-FISH classification techniques that do not use segmentation information.
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