B. Lerner et al., MEDIAL AXIS TRANSFORM-BASED FEATURES AND A NEURAL-NETWORK FOR HUMAN-CHROMOSOME CLASSIFICATION, Pattern recognition, 28(11), 1995, pp. 1673-1683
Citations number
21
Categorie Soggetti
Computer Sciences, Special Topics","Engineering, Eletrical & Electronic","Computer Science Artificial Intelligence
Medial axis transform (MAT) based features and a multilayer perceptron
(MLP) neural network (NN) were used for human chromosome classificati
on. Two approaches to the MAT, one based on skeletonization and the ot
her based on a piecewise linear (PWL) approximation, were examined. Th
e former yielded a finer medial axis, as well as better chromosome cla
ssification performances. Geometrical along with intensity-based featu
res were extracted and tested. The probability of correct training set
classification of five chromosome types was 99.3-99.6%. The probabili
ty of correct test set classification was greater than 98% and greater
than 97% using features extracted by the first and second approaches,
respectively. It was found that only 5-10, out of all the considered
features, were required to correctly classify the chromosomes with alm
ost no performance degradation.