title: Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images.
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.
authors.affiliation: Integrity Applications Inc., 5180 Parkstone Drive, Suite 260 Chantilly, VA 20151, USA. tmi1206.utwade@spam-gourmet.com
authors.forename: Wade C
authors.initials: WC
authors.lastname: Schwartzkopf
authors.forename: Alan C
authors.initials: AC
authors.lastname: Bovik
authors.forename: Brian L
authors.initials: BL
authors.lastname: Evans
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002877:Chromosomes, Human / Q000145:classification / Q000235:genetics* / Q000648:ultrastructure*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D016013:Likelihood Functions; D036641:Microscopy, Fluorescence, Multiphoton / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013050:Spectrometry, Fluorescence / Q000379:methods*