Automated chromosome classification limitations due to image processing.
Automated chromosome classification from metaphase spreads has been a difficult research problem over the past 30 years. Numerous techniques have been implemented to address the research problem. Image processing techniques support many methods utilized for automated and/or semiautomated karyotyping. Some current systems correctly classify individual chromosomes at high rates but lack the capability to properly classify chromosomes for entire cells consistently. Most systems attain high classification rates ignoring overlapping metaphase chromosomes for testing purposes. Chromosome classification depends on identifying features common to each chromosome class or number. Some of the common features incorporated into various chromosome classification systems include length, centromere location, banding pattern, and width. To complicate classification, chromosome features tend to vary not only between people but from cell to cell for the same person. Additionally, chromosomes found in metaphase spreads may have any orientation and virtually any degree of overlap with other chromosomes present. Besides the inherent barriers impeding chromosome classification, many systems perform image processing operations to the chromosomes for feature determination. Image processing operations, such as image rotation, that manipulate the chromosome grey-level information may distort the feature calculations utilized in karyotyping. Consequently, feature stability becomes an important issue for improving karyotyping capability.
Stanley
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R
University of Missouri-Columbia Medical Informatics Group 65211, USA.
Keller
J
J
Gader
P
P
Caldwell
C W
CW
D002877:Chromosomes, Human / Q000145:classification* / Q000648:ultrastructure; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007621:Karyotyping