Segmentation of complementary dna microarray images using the fuzzy gaussian mixture model technique
Σπυρίδωνος, Παναγιώτα Π.
Αθανασιάδης, Εμμανουήλ Ι.
Κάβουρας, Διονύσης Α.
Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.
Νικηφορίδης, Γεώργιος Χ.
The objective of this work was to investigate
the segmentation ability of the Fuzzy Gaussian Mixture
Models (FGMM) clustering algorithm, applied on
complementary DNA (cDNA) images. A Simulated
Microarray image of 200 cells, each containing one spot, was
produced following standard established procedure. An
automatic gridding process was developed and applied on
the microarray image for the task of locating spot borders
and surrounding background in each cell. The FGMM and
the Gaussian Mixture Model (GMM) algorithms were
applied to each cell, with the purpose of discriminating
foreground from background. The segmentation abilities of
both algorithms were evaluated by means of the
segmentation matching factor in respect to the actual classes
(foreground-background pixels) of the simulated spots. The
FGMM was found to perform better and with equal
processing time, as compared to the GMM, rendering the
FGMM algorithm an efficient alternative for segmenting
cDNA microarray images.
Technological Educational Institute of Athens
International Special Topic Conference on Information Technology in Biomedicine