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 language | English |
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Pages (from-to) | 1673-1683 |
Number of pages | 11 |
Journal | Pattern Recognition |
Volume | 28 |
Issue number | 11 |
DOIs | |
State | Published - 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