Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images

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%.

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

“…The classification accuracy of this k-Means clustering and Otsu thresholding scheme was confirmed to be predominant by 13% and 18% compared to the graph search and graph cut-based segmentation method. In addition, a cervical cancer detection scheme using k-Means and simple linear iterative clustering process was contributed to improving the accuracy in segmenting cervical pap smear images [18]. In [19], a two-phase approach for cell segmentation in Pap smear test images was proposed.…”

A machine learning model for screening of body fluid cytology smears

“…On a sample of 125 imayes, their method achieved sensitivity of 87.97%, specifcity of 99.40%, with 98.70% diaynostic accuracy. [22] Baykal et al used the technique of active appearance model for to achieve efective cell seymentation from cytopatholoyical imayes, with yood diaynostic accuracy. [23] Te wavelet transform has also been shown to achieve a hiyh recoynition ratio.…”

A Survey for Cervical Cytopathology Image Analysis Using Deep Learning

“…Next, features (morphological, color metric, texture) are extracted from each nucleus, and feature selection is applied to select the most discriminant feature. Finally, a classifier is designed to classify the cell [25].…”

“…Most of them are based on machine learning techniques. Particular examples are automated cell nuclei segmentation [126], detection of cancer cells [127], [128], segmentation, and isolation of touching nuclei [129]. Next, the nucleus segmentation of breast fine-needle aspiration cytology (FNAC) images will be a handy field in this regard [130]- [133].…”