title: Computing maximum association graph in microscopic nucleus images.
abstract: In this paper, we study the problem of finding organization patterns of chromosomes inside the cell nucleus from microscopic nucleus images. Emerging evidence from cell biology research suggests that global chromosome organization has a vital role in fundamental cell processes related to gene expression and regulation. To understand how chromosome territories are neighboring (or associated) to each other, in this paper we present a novel technique for computing a common association pattern, represented as a Maximum Association Graph (MAG), from the nucleus images of a population of cells. Our approach is based on an interesting integer linear programming formulation of the problem and utilizes inherent observations of the problem to yield optimal solutions. A two-stage technique is also introduced for producing near optimal approximations for large data sets.
authors.affiliation: Department of Computer Science and Engineering, State University of New York at Buffalo, USA. bs65@buffalo.edu
authors.forename: Branislav
authors.initials: B
authors.lastname: Stojkovic
authors.forename: Yongding
authors.initials: Y
authors.lastname: Zhu
authors.forename: Jinhui
authors.initials: J
authors.lastname: Xu
authors.forename: Andrew
authors.initials: A
authors.lastname: Fritz
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Zeitz
authors.forename: Jaromira
authors.initials: J
authors.lastname: Vecerova
authors.forename: Ronald
authors.initials: R
authors.lastname: Berezney
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002875:Chromosomes / Q000648:ultrastructure*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity