Automatic Gridding for DNA Microarray Image Using Image Projection Profile
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Automatic Gridding for DNA Microarray Image Using Image Projection Profile


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Abstract

DNA microarray is powerful tool and widely used in many areas.
DNA microarray is produced from control and test tissue sample cDNAs, which
are labeled with two different fluorescent dyes. After hybridization using a laser
scanner, microarray images are obtained. Image analysis play an important role
in extracting fluorescence intensity from microarray image. First step in
microarray image analysis is addressing, that is finding areas in the image on
which contain one spot using gird lines. This step can be done by either
manually or automatically. In this paper we propose an efficient and simple
automatic gridding for microarray image analysis using image projection profile,
base on fact that microarray image has local minimum and maximum intensity
at background and foreground areas respectively. Grid lines are obtained by
finding local minimum of vertical and horizontal projection profile. This
algorithm has been implemented in MATLAB and tested with several
microarray images.

Introduction

DNA microarray is powerful tool and widely used in many areas, e.g. for
human genetic research and drug discovery. DNA microarray is produced from
control and test tissue sample cDNAs, which are labeled with two different
fluorescent dyes, usually the red fluorescent dye Cy5 for the control and green
fluorescent dye Cy3 for the test and then printed on a glass microslide
containing gene specific cDNA clones arranged in an array format for
hybridization. After hybridization using a laser scanner, images of the
microarray are obtained [1].

Conclusion

First step in microarray image analysis is addressing, that is finding areas in
the image on which contain one spot using gird lines. This step is one of the
most important step in microarray image analysis and can be done by either
manually or automatically. The Gridclus algorithm is an algorithm for
microarray image automatic gridding base on k-mean clustering has been
presented, but this algorithm is not efficient in time computing. Another
algorithm uses mathematical morphology, including image projection profile
and other complex mathematical morphology operations. An efficient and
simple automatic gridding for microarray image analysis using image
projection profile has introduced. This algorithm base on fact that microarray
image has local minimum and maximum intensity at background and
foreground areas respectively.
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