Publication: Detection and classification of overlapping cell nuclei in cytology effusion images using a double-strategy random forest
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
20763417
Other identifier(s)
2-s2.0-85053150206
Rights Holder(s)
มหาวิทยาลัยศรีนครินทรวิโรฒ
Bibliographic Citation
Applied Sciences (Switzerland). Vol 8, No.9 (2018)
Suggested Citation
Win K.Y., Choomchuay S., Hamamoto K., Raveesunthornkiat M. Detection and classification of overlapping cell nuclei in cytology effusion images using a double-strategy random forest. Applied Sciences (Switzerland). Vol 8, No.9 (2018). doi:10.3390/app8091608 Retrieved from: https://hdl.handle.net/20.500.14740/5848
Author(s)
Abstract
Due to the close resemblance between overlapping and cancerous nuclei, the misinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus, it is essential to detect overlapping nuclei and distinguish them from single ones for subsequent quantitative analyses. This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The proposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction, and classification of single and overlapping nuclei. A maximum entropy thresholding method complemented by image enhancement and post-processing was employed for nuclei candidate extraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was extracted from each nucleus region. A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones using selected features. The proposed method was evaluated on 4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity, 98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under curve. The computation time required to run the entire algorithm was just 5.17 s. The experiment results demonstrate that the proposed algorithm yields a superior performance to previous studies and other classifiers. The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images. © 2018 by the authors.