Automated classification of metaphase chromosomes: optimization of an adaptive computerized scheme

Abstract

We developed and tested a new automated chromosome karyotyping scheme using a two-layer classification platform. Our hypothesis is that by selecting most effective feature sets and adaptively optimizing classifiers for the different groups of chromosomes with similar image characteristics, we can reduce the complexity of automated karyotyping scheme and improve its performance and robustness. For this purpose, we assembled an image database involving 6900 chromosomes and implemented a genetic algorithm to optimize the topology of multi-feature based artificial neural networks (ANN). In the first layer of the scheme, a single ANN was employed to classify 24 chromosomes into seven classes. In the second layer, seven ANNs were adaptively optimized for seven classes to identify individual chromosomes. The scheme was optimized and evaluated using a "training-testing-validation" method. In the first layer, the classification accuracy for the validation dataset was 92.9%. In the second layer, classification accuracy of seven ANNs ranged from 67.5% to 97.5%, in which six ANNs achieved accuracy above 93.7% and only one had lessened performance. The maximum difference of classification accuracy between the testing and validation datasets is <1.7%. The study demonstrates that this new scheme achieves higher and robust performance in classifying chromosomes.

Figure 1

(a) A metaphase spread image and (b) the corresponding karyotype image.

Figure 2

A flow diagram of automated classification of chromosomes.

Figure 3

(a) Illustration of an ideogram of chromosome #1 and a real chromosome #1, (b) several medial axis detection results of chromosome #1 with different morphologies obtained by a modified thinning algorithm.

Figure 4

(a) a density profile of chromosome #22, (b) a density profile of chromosome #10, (c) a density profile of chromosome #1, (d) a shape profile of chromosome #22, (e) a shape profile of chromosome #10, (f) a shape profile of chromosome #1, (g) an example of chromosome #19, (h) an original banding profile, (i) a reversed banding profile, (j) an idealized density profile gained by a non-linear file. Note: G is the gray value of all pixels in each perpendicular line across the medial axis of a chromosome; W is the width of a chromosome; L is the length of a chromosome.

Figure 5

Display of eight weighted functions.

Figure 6

Illustration of an ANN-based decision tree classifier used to classify chromosomes.

Figure 7

(a) Illustration of an ANN optimized by GA in the first layer, (b) Illustration of an ANN optimized by GA in the second layer.

Figure 8

Distribution of the feature selected in 7 ANNs.