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                                           BIOINFORMATICS COLLOQUIUM
					       College of Science
                                            George Mason University
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                      Novel Approaches to Cell and Tissue Image Analysis, and its Applications 
                                         to Genomic Analysis of Breast Cancer
				
                               	                Stephen Lockett, Ph.D.
 
					         Principal Scientist
					      National Cancer Institute

Abstract:
Segmentation of individual cells and cell nuclei from microscope images continues 
to be a challenging task, and the appropriate strategy is dependent on the 
biological application.  For understanding communications between cells that 
drive tissue development and function, as well as disease-related processes such 
as tumorigenesis, we have developed highly reliable and accurate semi-automatic 3D 
algorithms.  For high throughput screening of nuclei from 2D cell culture images, we 
have developed model-free segmentation from which the most frequent class of objects 
in the image is automatically modeled using statistical pattern recognition.  This 
software adapts on the fly to changes between datasets in the characteristics of the 
imaged nuclei.  For automatic analysis of cell nuclei in tissue, we have developed an 
intelligent framework coupling a hybrid nuclei segmentation algorithm with pattern 
recognition algorithms to automatically identify well segmented
nuclei. Application of this software with spatial statistical analysis of the FISH 
spots indicates encouraging preliminary results for diagnosing breast cancer based on 
the positioning of certain genes in cancer cell nuclei versus the nuclei of normal cells.