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Appl. Sci. 2018, 8(9), 1608; https://doi.org/10.3390/app8091608

Detection and Classification of Overlapping Cell Nuclei in Cytology Effusion Images Using a Double-Strategy Random Forest

1
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
School of Information and Telecommunication Engineering, Tokai University, Tokyo 108-8619, Japan
3
Department of Pathology, Faculty of Medicine, Srinakharinwirot University, Nakhon Nayok 26000, Thailand
*
Author to whom correspondence should be addressed.
Received: 2 August 2018 / Revised: 3 September 2018 / Accepted: 3 September 2018 / Published: 11 September 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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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. View Full-Text
Keywords: pleural effusion; automatic cell analysis; overlapping nuclei; maximum entropy thresholding; geometric features; textural features; random forest pleural effusion; automatic cell analysis; overlapping nuclei; maximum entropy thresholding; geometric features; textural features; random forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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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. Appl. Sci. 2018, 8, 1608.

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