Dose from radiation exposure can be estimated from dicentric chromosome (DC) frequencies in metaphase cells of peripheral blood lymphocytes. We automated DC detection by extracting features in Giemsa-stained metaphase chromosome images and classifying objects by machine learning (ML). DC detection involves (i) intensity thresholded segmentation of metaphase objects, (ii) chromosome separation by watershed transformation and elimination of inseparable chromosome clusters, fragments and staining debris using a morphological decision tree filter, (iii) determination of chromosome width and centreline, (iv) derivation of centromere candidates, and (v) distinction of DCs from monocentric chromosomes (MC) by ML. Centromere candidates are inferred from 14 image features input to a Support Vector Machine (SVM). Sixteen features derived from these candidates are then supplied to a Boosting classifier and a second SVM which determines whether a chromosome is either a DC or MC. The SVM was trained with 292 DCs and 3135 MCs, and then tested with cells exposed to either low (1 Gy) or high (2-4 Gy) radiation dose. Results were then compared with those of 3 experts. True positive rates (TPR) and positive predictive values (PPV) were determined for the tuning parameter, σ. At larger σ, PPV decreases and TPR increases. At high dose, for σ=1.3, TPR=0.52 and PPV=0.83, while at σ=1.6, the TPR=0.65 and PPV=0.72. At low dose and σ=1.3, TPR=0.67 and PPV=0.26. The algorithm differentiates DCs from MCs, overlapped chromosomes and other objects with acceptable accuracy over a wide range of radiation exposures. Microsc. Res. Tech. 79:393-402, 2016.

Additional Metadata
Keywords Biodosimetry, Cytogenetics, Radiation exposure, Software development, Support vector machines
Persistent URL dx.doi.org/10.1002/jemt.22642
Journal Microscopy Research and Technique
Citation
Li, Y. (Yanxin), Knoll, J.H. (Joan H.), Wilkins, R.C, Flegal, F.N. (Farrah N.), & Rogan, P.K. (Peter K.). (2016). Automated discrimination of dicentric and monocentric chromosomes by machine learning-based image processing. Microscopy Research and Technique, 79(5), 393–402. doi:10.1002/jemt.22642