New features for automatic classification of human chromosomes: A feasibility study

Abstract

Karyotyping, a standard method for presenting pictures of the human chromosomes for diagnostic purposes, is a long standing, yet common technique in cytogenetics. Automating the chromosome classification process is the first step in designing an automatic karyotyping system. The main aim in this study was to define a new group of features for better representation and classification of chromosomes. Width, position and the average intensity of the two most eye-catching regions of each chromosome (that we call characteristic bands) are the new proposed features. The concept of a characteristic band is based on the expert cytogeneticists’ method in classification of the chromosomes. The length, centromeric index (CI) and an index of overall darkness or brightness of the image (NAGD) were also included in the final nine-dimensional feature vectors describing each chromosome. To automatically find the characteristic bands and calculate the new features, different windows in chromosome’s density profile were scored based on their intensity and width. As a feasibility study, our work was focused on classification of chromosomes in group E. Three layer artificial neural networks were employed to classify each chromosome in one of the three possible classes (chromosomes 16, 17 and 18). The best results obtained were accurate classification of up to 98.6% of chromosomes. Particularly a six-dimensional subset of the features showed reproducibly high performances in classification experiments. The results of this feasibility study show that new features inspired from human expert’s classification method are potentially capable of improving the accuracy of the karyotyping systems.