Enhancement of the classification of multichannel chromosome images using support vector machines.
Color chromosome classification (karyotyping) allows simultaneous analysis of numerical and structural chromosome abnormalities. The success of the technique largely depends on the accuracy of pixel classification. In this paper we present a method for multichannel chromosome image classification based on support vector machines. First, the image is segmented using a multichannel watershed segmentation method. Classification of the pixels of the segmented regions using support vector machines is then employed. The method has been tested on images from normal cells, showing the improvement in classification accuracy by 10.16% when compared to a Bayesian classifier. The increased classification improves the reliability of the M-FISH imaging technique in identifying subtle and cryptic chromosomal abnormalities for cancer diagnosis and genetic disorders research.
Karvelis
P S
PS
Dept. of Computer Science, University of Ioannina, Ioannina, Greece, GR 45110. pkarvel@cs.uoi.gr
Fotiadis
D I
DI
Georgiou
I
I
Sakaloglou
P
P
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000295:instrumentation* / Q000379:methods; D002875:Chromosomes / Q000648:ultrastructure*; D003952:Diagnostic Imaging / Q000295:instrumentation / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods; D017404:In Situ Hybridization, Fluorescence / Q000295:instrumentation* / Q000379:methods; D008856:Microscopy, Fluorescence / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000145:classification / Q000379:methods