Fuzzy-Based Analysis of Microscopic Color Cervical Pap Smear Images: Nuclei Detection

Abstract

The Pap Smear test, or cervico-vaginal cytology, is globally the most used and suitable method for screening cervical cancer precursor lesions, with a significant impact in reducing the incidence and mortality rates. However, Pap test suffers from subjective variability and no specificity, being the most controversial point the persistence of false negatives; i.e., normal cytological report for a woman with existing dysplasia, pre-malignant, or malignant lesions of the cervix. This is due in large part to the vast number of cells that must be reviewed by a technician for determining the possible existence of a small number of malignant or pre-malignant cells. Automated systems that include technician knowledge and interpretation could not only reduce sample examination time but also avoid misclassification of samples because of human errors. Here we present part of our ongoing work toward automation of cervical screening process. Specifically, since in cytological studies nuclei are considered the most informative regions, and an accurate segmentation is needed for extracting meaningful cell features, we propose an automated nuclei detection algorithm that integrates color information, cytopathologists knowledge, and fuzzy systems. Results have shown that besides a high performance and efficiency, the speed of the algorithm is very high.

Automated Detection of Dual p16/Ki67 Nuclear Immunoreactivity in Liquid-Based Pap Tests for Improved Cervical Cancer Risk Stratification

“…The proposed method analyzes features solely from cell nuclei that constitute small, yet detectable and quantifiable fraction of the whole specimen. This distinguishes our approach from other techniques in which the delineation of cytoplasmic and nuclear areas are required [5-7, 10]. …”

“…Major factors contributing to this effect are differences in shape and intensity between the adjacent nuclei. Despite several shortcomings our nuclei segmentation procedure appears to be well suited for the aforesaid purpose due to the following reasons: a) we extensively evaluated a much larger number of nuclei compared to other studies [5], [6] and [9], b) it maintains a high detection rate; with precision >93.3% and recall >92.8% regardless of the staining intensity compared to the 90% average effectiveness shown in [7] and recall of 90.57% or 69.86% in [8], and c) it shows good nuclear area agreement when measured against two human observers. Although the segmentation provides accurate outlines for the majority of nuclei, there were instances with partially encircled cytoplasm.…”

“…Moreover, the contours obtained by [6] were sensitive to local image contrast and resulted in split outlines of hypochromatic nuclei. An approach presented in [7] utilized color images of smears in combination with fuzzy logic and a priori human knowledge for nuclear segmentation. Although the detection efficacy was generally high, it was significantly decreased for nuclei with low optical density.…”

Multi Class Cervical Cancer Classification by using ERSTCM, EMSD & CFE methods based Texture Features and Fuzzy Logic based Hybrid Kernel Support Vector Machine Classifier

“…Using morphological analysis, the nuclei centroids are detected by applying distance dependent rule and classification algorithms on resulted centroids, the undesirable artifacts were removed from the cell. By considering nucleus as the most informative region of the cell, Sobrevilla et al [8] have been presented an algorithm for automatic nuclei detection of cytology cell. This algorithm combines color, cyto pathologist"s knowledge, and fuzzy systems which show high performance and more computational speed.…”

A Colour Space Based Detection for Cervical Cancer Using Fuzzy C-Means Clustering

“…The cell nucleus is a structural part of the cell that can show significant changes when the cell is exposed to the disease. Most malignant characteristics of cells contain in the cell nucleus so that the cell nucleus is widely used in cell classification standard such as The Bathesda System (TBS) [6]. Because of the importance of the nuclei, so the cell nuclei segmentation accuracy becomes the important task in developing the detection system of cervical cancer.…”