Maximum-likelihood techniques for joint segmentation-classification of multispectral chromosome images

• Published in: IEEE transactions on medical imaging Vol. 24; no. 12; pp. 1593 - 1610
• Main Authors: Schwartzkopf, W.C., Bovik, A.C., Evans, B.L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-12-2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Biological cells; Cancer; Chromosomes; Chromosomes, Human - classification; Chromosomes, Human - genetics; Chromosomes, Human - ultrastructure; Diseases; Gray-scale; Humans; Identification systems; Image analysis; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; In Situ Hybridization, Fluorescence - methods; karyotyping; Labeling; Likelihood Functions; Microscopy, Fluorescence, Multiphoton - methods; Models, Genetic; Models, Statistical; Multispectral imaging; object recognition; partial occlusion; Pattern Recognition, Automated - methods; Reproducibility of Results; Robustness; Sensitivity and Specificity; Spectrometry, Fluorescence - methods; System testing
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2005.859207

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.