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

[+] Author Affiliations
Xingwei Wang

University of Oklahoma, Center for Bioengineering, and, School of Electrical and Computer Engineering, Norman, Oklahoma 73019

Bin Zheng

University of Pittsburgh Medical Center, Department of Radiology, Pittsburgh, Pennsylvania 15213

Shibo Li, John J. Mulvihill

University of Oklahoma, Health Science Center, Department of Pediatrics, Oklahoma City, Oklahoma 73104

Hong Liu

University of Oklahoma, Center for Bioengineering, and, School of Electrical and Computer Engineering, 202 West Boyd Street, Room 219, Norman, Oklahoma 73019

J. Electron. Imaging. 17(4), 043008 (November 12, 2008). doi:10.1117/1.3013459
History: Received January 17, 2008; Revised June 18, 2008; Accepted August 26, 2008; Published November 12, 2008
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An integrated computer-aided detection (CAD) scheme was developed for detecting and classifying metaphase chromosomes. The CAD scheme’s performance and robustness is assessed. 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 the 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 with 86.7% in the previous study using two independent datasets randomly acquired from our genetic laboratory. The results demonstrate that this automated scheme achieves high and robust performance in identification and classification of metaphase chromosomes.

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Citation

Xingwei Wang ; Bin Zheng ; Shibo Li ; John J. Mulvihill and Hong Liu
"Development and assessment of an integrated computer-aided detection scheme for digital microscopic images of metaphase chromosomes", J. Electron. Imaging. 17(4), 043008 (November 12, 2008). ; http://dx.doi.org/10.1117/1.3013459


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