Medial axis transform-based features and a neural network for human chromosome classification

摘要

Medial axis transform (MAT) based features and a multilayer perceptron (MLP) neural network (NN) were used for human chromosome classification. Two approaches to the MAT, one based on skeletonization and the other based on a piecewise linear (PWL) approximation, were examined. The former yielded a finer medial axis, as well as better chromosome classification performances. Geometrical along with intensity-based features were extracted and tested. The probability of correct training set classification of five chromosome types was 99.3–99.6%. The probability 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 almost no performance degradation.