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%.
Highlights
Pleural effusion or pulmonary effusion (PE) is the pathologic accumulation of fluid in the pleural cavity, between the visceral and parietal layers surrounding the lung, as demonstrated in Figure 1 [1, 2]
The results demonstrate that, with the exception of coupling with Support Vector Machine (SVM), K-nearest neighborhood (KNN), and Logistic Regression (LR) classifiers, the proposed SA-artificial neural network (ANN) selection marginally improves accuracy compared to the SA based approach and yields better accuracy compared to PSO-ANN and GA-ANN approaches when coupling with ANN, Naıve Bayes (NB), LD, Decision Tree (DT), and proposed ECBDT classifiers
We presented a novel Computer Aided Diagnosis (CAD) system to detect cancer cells on cytological pleural effusion (CPE) images
Summary
Pleural effusion or pulmonary effusion (PE) is the pathologic accumulation of fluid in the pleural cavity, between the visceral and parietal layers surrounding the lung, as demonstrated in Figure 1 [1, 2]. When cancer cells grow or spread to the pleura, they cause malignant pleural effusion (MPE). Half of all cancer patients have a high possibility of developing MPE. Both primary and metastatic cancers can lead to a diagnosis of MPE. Lung cancer and breast cancer are the most frequent metastatic cancers in male and female patients, respectively. Both malignancies are responsible for about 5065% of MPE. As mentioned earlier, MPE is mostly caused by the invasion of metastatic cancer to the pleura. Cancer in the pleural effusion is seen in advanced stages of malignancy and leads to rapid mortality, the survival time can be prolonged by earlier diagnosis
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