ISSN:
1573-1375
Keywords:
Automation
;
chromosome
;
classification
;
karyotype
;
neural network
;
pattern recognition
;
review
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mathematics
Notes:
Abstract Computer-aided imaging systems are now widely used in cytogenetic laboratories to reduce the tedium and labour-intensiveness of traditional methods of chromosome analysis. Automatic chromosome classification is an essential component of such systems, and we review here the statistical techniques that have contributed towards it. Although completely error-free classification has not been, nor is ever likely to be, achieved, error rates have been reduced to levels that are acceptable for many routine purposes. Further reductions are likely to be achieved through advances in basic biology rather than in statistical methodology. Nevertheless, the subject remains of interest to those involved in statistical classification, because of its intrinsic challenges and because of the large body of existing results with which to compare new approaches. Also, the existence of very large databases of correctly-classified chromosomes provides a valuable resource for empirical investigations of the statistical properties of classifiers.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00142568
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