This dissertation was presented to the Faculty of the Graduate School of The University of Texas at Austin in partial fulfillment of the requirements for the degree of

Ph.D. in Electrical Engineering


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


Wade Schwartzkopf, Ph.D.E.E.

The University of Texas at Austin, December 2002


Prof. Brian L. Evans
Prof. Alan C. Bovik



Dissertation - Defense (PowerPoint) - Defense (PDF) - Software


This dissertation develops new methods for automatic chromosome identification by taking advantage of the multispectral information n M-FISH chromosome images and by jointly performing chromosome segmentation and classification. Chromosome imaging is a valuable tool for doctors and cytogenetic technicians. Extra chromosomes, missing chromosomes, broken chromosomes, and translocations (parts of chromosomes breaking off and attaching to other chromosomes) are indicators of radiation damage, cancer, and a wide variety of inherited diseases. There are currently over 325 clinical cytogenetics laboratories in the United States performing over 250,000 diagnostic studies each year involving chromosome analysis.

Traditional chromosome imaging has been limited to grayscale images, but recently a 5-fluorophore combinatorial labeling technique (M-FISH) was developed in which each class of chromosomes binds with a different combination of fluorophores. This results in a multi-spectral image, in which each class of chromosomes has distinct spectral components. Although M-FISH presents significantly more information than was available in traditional grayscale images, little research on multispectral chromosome image analysis has been previously reported in the open literature.

The purpose of the research described in this dissertation is to develop new methods for automatic chromosome identification. In particular, I (1) develop a maximum likelihood hypothesis test that uses this 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 the chromosomes anomalies that can be diagnosed with M-FISH imaging. I show that the proposed multi-spectral joint segmentation-classification method outperforms past grayscale segmentation methods in decomposing touching chromosomes. Furthermore, I show that it outperforms past M-FISH classification techniques that do not use segmentation information.


For more information contact: Wade Schwartzkopf.