Computing maximum association graph in microscopic nucleus images.
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.
Stojkovic
Branislav
B
Department of Computer Science and Engineering, State University of New York at Buffalo, USA. bs65@buffalo.edu
Zhu
Yongding
Y
Xu
Jinhui
J
Fritz
Andrew
A
Zeitz
Michael J
MJ
Vecerova
Jaromira
J
Berezney
Ronald
R
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