Publication: Comparative study on automated cell nuclei segmentation methods for cytology pleural effusion images
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
20402295
Other identifier(s)
2-s2.0-85054038862
Rights Holder(s)
Scopus
Bibliographic Citation
Journal of Healthcare Engineering. Vol 2018, (2018)
Suggested Citation
Win K.Y., Choomchuay S., Hamamoto K., Raveesunthornkiat M. Comparative study on automated cell nuclei segmentation methods for cytology pleural effusion images. Journal of Healthcare Engineering. Vol 2018, (2018). doi:10.1155/2018/9240389 Retrieved from: https://hdl.handle.net/20.500.14740/3944
Author(s)
Abstract
Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. Studies on the automated analysis of cytology pleural effusion images are few because of the lack of reliable cell nuclei segmentation methods.Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images.Each method involves three main steps: preprocessing,segmentation, and postprocessing.The preprocessing and segmentation stages help enhancing the image quality and extracting the nuclei regions from the rest of the image, respectively.The postprocessing stage helps in refining the segmented nuclei and removing false findings.The segmentation methods are quantitatively evaluated for 35 cytology images of pleural effusion by computing five performance metrics. The evaluation results show that the segmentation performances of the Otsu, k-means, mean shift, Chan-Vese,and graph cut methods are 94,94,95,94,and 93%,respectively, with high abnormal nuclei detection rates.The average computational times per image are 1.08,36.62,50.18,330, and 44.03 seconds,respectively.The findings of this study will be useful for current and potential future studies on cytology images of pleural effusion. ©2018 Khin Yadanar Win et al.
Subject(s)
Automation
Cells
Computer aided diagnosis
Cytology
Graphic methods
Image enhancement
Quality control
Cell nuclei segmentation
Comparative studies
Computer aided diagnosis systems
Nuclei segmentation
Performance metrics
Post-processing stages
Segmentation methods
Segmentation performance
Image segmentation
Article
Automation
Benchmarking
Cell nucleus
Cytology
Evaluation study
Gold standard
Human
Human tissue
Intermethod comparison
Mathematical model
Pleura effusion
Quantitative analysis
Algorithm
Cluster analysis
Comparative study
Computer assisted diagnosis
Cytodiagnosis
Image processing
Pleura effusion
Procedures
Reproducibility
Software
Algorithms
Cell Nucleus
Cluster Analysis
Cytodiagnosis
Cytological Techniques
Diagnosis, Computer-Assisted
Humans
Image Processing, Computer-Assisted
Pleural Effusion
Reproducibility of Results
Software
Cells
Computer aided diagnosis
Cytology
Graphic methods
Image enhancement
Quality control
Cell nuclei segmentation
Comparative studies
Computer aided diagnosis systems
Nuclei segmentation
Performance metrics
Post-processing stages
Segmentation methods
Segmentation performance
Image segmentation
Article
Automation
Benchmarking
Cell nucleus
Cytology
Evaluation study
Gold standard
Human
Human tissue
Intermethod comparison
Mathematical model
Pleura effusion
Quantitative analysis
Algorithm
Cluster analysis
Comparative study
Computer assisted diagnosis
Cytodiagnosis
Image processing
Pleura effusion
Procedures
Reproducibility
Software
Algorithms
Cell Nucleus
Cluster Analysis
Cytodiagnosis
Cytological Techniques
Diagnosis, Computer-Assisted
Humans
Image Processing, Computer-Assisted
Pleural Effusion
Reproducibility of Results
Software