Computing maximum association graph in microscopic nucleus images

Authors:
Branislav Stojkovic;Yongding Zhu;Jinhui Xu;Andrew Fritz;Michael J. Zeitz;Jaromira Vecerova;Ronald Berezney
Affiliations:
Department of Computer Science and Engineering, State University of New York at Buffalo;Department of Computer Science and Engineering, State University of New York at Buffalo;Department of Computer Science and Engineering, State University of New York at Buffalo;Department of Biological Sciences, State University of New York at Buffalo;Stanford Medical School, Palo Alto, CA;Department of Biological Sciences, State University of New York at Buffalo;Department of Biological Sciences, State University of New York at Buffalo
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Year:
2010

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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.