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Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images
Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Ai...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6250027/ https://www.ncbi.nlm.nih.gov/pubmed/30533436 http://dx.doi.org/10.1155/2018/6456724 |
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author | Win, Khin Yadanar Choomchuay, Somsak Hamamoto, Kazuhiko Raveesunthornkiat, Manasanan Rangsirattanakul, Likit Pongsawat, Suriya |
author_facet | Win, Khin Yadanar Choomchuay, Somsak Hamamoto, Kazuhiko Raveesunthornkiat, Manasanan Rangsirattanakul, Likit Pongsawat, Suriya |
author_sort | Win, Khin Yadanar |
collection | PubMed |
description | Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%. |
format | Online Article Text |
id | pubmed-6250027 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-62500272018-12-09 Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images Win, Khin Yadanar Choomchuay, Somsak Hamamoto, Kazuhiko Raveesunthornkiat, Manasanan Rangsirattanakul, Likit Pongsawat, Suriya Biomed Res Int Research Article Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%. Hindawi 2018-11-08 /pmc/articles/PMC6250027/ /pubmed/30533436 http://dx.doi.org/10.1155/2018/6456724 Text en Copyright © 2018 Khin Yadanar Win et al. https://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | Research Article Win, Khin Yadanar Choomchuay, Somsak Hamamoto, Kazuhiko Raveesunthornkiat, Manasanan Rangsirattanakul, Likit Pongsawat, Suriya Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images |
title | Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images |
title_full | Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images |
title_fullStr | Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images |
title_full_unstemmed | Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images |
title_short | Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images |
title_sort | computer aided diagnosis system for detection of cancer cells on cytological pleural effusion images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6250027/ https://www.ncbi.nlm.nih.gov/pubmed/30533436 http://dx.doi.org/10.1155/2018/6456724 |
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