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

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48 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1673-1683
Number of pages11
JournalPattern Recognition
Volume28
Issue number11
DOIs
StatePublished - 1 Jan 1995

Keywords

  • Chromosome classification
  • Medial axis transform
  • Neural networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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