Publication: Computer aided diagnosis system for detection of cancer cells on cytological pleural effusion images
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
23146133
Other identifier(s)
2-s2.0-85057083571
Rights Holder(s)
Scopus
Bibliographic Citation
BioMed Research International. Vol 2018, (2018)
Suggested Citation
Win K.Y., Choomchuay S., Hamamoto K., Raveesunthornkiat M., Rangsirattanakul L., Pongsawat S. Computer aided diagnosis system for detection of cancer cells on cytological pleural effusion images. BioMed Research International. Vol 2018, (2018). doi:10.1155/2018/6456724 Retrieved from: https://hdl.handle.net/20.500.14740/3947
Abstract
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%. © 2018 Khin Yadanar Win et al.
Subject(s)
Article
Artificial neural network
Cancer cell
Cell nucleus
Classifier
Colorimetry
Computer assisted diagnosis
Cytodiagnosis
Decision tree
Image quality
Pleura effusion
Algorithm
Computer assisted diagnosis
Cytology
Human
Image processing
Pathology
Pleura effusion
Pleura tumor
Procedures
Receiver operating characteristic
Tumor cell line
Algorithms
Cell Line, Tumor
Cell Nucleus
Cytological Techniques
Decision Trees
Diagnosis, Computer-Assisted
Humans
Image Processing, Computer-Assisted
Pleural Effusion
Pleural Neoplasms
ROC Curve
Artificial neural network
Cancer cell
Cell nucleus
Classifier
Colorimetry
Computer assisted diagnosis
Cytodiagnosis
Decision tree
Image quality
Pleura effusion
Algorithm
Computer assisted diagnosis
Cytology
Human
Image processing
Pathology
Pleura effusion
Pleura tumor
Procedures
Receiver operating characteristic
Tumor cell line
Algorithms
Cell Line, Tumor
Cell Nucleus
Cytological Techniques
Decision Trees
Diagnosis, Computer-Assisted
Humans
Image Processing, Computer-Assisted
Pleural Effusion
Pleural Neoplasms
ROC Curve