IEEE Transactions on Medical Imaging, vol. 24, no. 12, pp. 1593-1610, Dec. 2005.

Maximum Likelihood Techniques for Joint Segmentation-Classification of Multi-spectral Chromosome Images

Wade Schwartzkopf, Alan C. Bovik, and Brian L. Evans

Department of Electrical and Computer Engineering, Engineering Science Building, The University of Texas at Austin, Austin, TX 78712-1084 -

Paper (IEEE Explore) - Draft of Paper - Software - MFISH Database

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 multi-spectral 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 multi-spectral 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 multi-spectral 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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Last Updated 08/02/11.