Development and assessment of an integrated computer-aided detection scheme for digital microscopic images of metaphase chromosomes

The authors developed an integrated computer-aided detection (CAD) scheme for detecting and classifying metaphase chromosomes as well as assessing its performance and robustness. This scheme includes an automatic metaphase-finding module and a karyotyping module and it was applied to a testing database with 200 digital microscopic images. The automatic metaphase-finding module detects analyzable metaphase cells using a feature-based artificial neural network (ANN). The ANN-generated outputs are analyzed by a receiver operating characteristics (ROC) method and an area under the ROC curve is 0.966. Then, the automatic karyotyping module classifies individual chromosomes of this cell into 24 types. In this module, a two-layer decision tree-based classifier with eight ANNs established in its connection nodes was optimized by a genetic algorithm. Chromosomes are first classified into seven groups by the ANN in the first layer. The chromosomes in these groups are then separately classified by seven ANNs into 24 types in the second layer. The classification accuracy is 94.5% in the first layer. Six ANNs achieved the accuracy above 95% and only one had lessened performance (80.6%) in the second layer. The overall classification accuracy is 91.5% as compared to 86.7% in the previous study using two independent datasets randomly acquired from our genetic laboratory. The results demonstrate that our automated scheme achieves high and robust performance in identification and classification of metaphase chromosomes.