Automated Extraction of Cytoplasm and Nuclei from Cervical Cytology Images by Fuzzy Thresholding and Active Contours

Abstract

In this paper, a novel method for automated diagnosis of cervical cancer by extracting cytoplasm and nuclei from cervical cytology images is described. The background is removed by preprocessing methods like Edge sharpening and Adaptive Histogram Equalization. Fuzzy thresholding and Active contours are used for extracting the region of interest containing the cytoplasm and nuclei. The nuclei are separated from the cytoplasm using linear contrast stretching. The nucleus to cytoplasm ratio is used to determine the stage of cancer.

Data cluster analysis-based classification of overlapping nuclei in Pap smear samples

“…Overlapped and adjacent nuclei regions appear mostly as larger, irregular objects in the samples [1]. That excessive growth in size occurs in malignant cases is a matter of a priori knowledge about nuclei in Pap smear samples [2]. Therefore a fully automated classification system for histological abnormalities should be able to differentiate and also separate overlapping/aggregating candidate objects.In this study we proposed a prerequisite approach for a fully automated separation system which involves a pre-classification system for advanced abnormality detection and interregional border extraction of nuclei.…”

“…Thus, one of the most common features that guide the detection of an existing malignancy is an increased nucleus-to-cytoplasm ratio [2]. Hence, one of the highest priority tasks for an automated Pap smear monitoring system is the segmentation of cell nuclei.…”

“…For instance, Jung et al reported an unsupervised Bayesian classification scheme for separating overlapping regions [2]. In another study, Li et al utilized a modified gradient vector flow [7], as well as radiating gradient vector flow (RGVF) snake and k-means unsupervised clustering methods, for the accurate extraction of overlapping cytoplasm and nuclei in their study.…”

“…In most previous studies, a typical analysis structure of an automated or semi-automated Pap smear diagnosis system includes segmentation of both the nuclei and cytoplasm regions [2, 3, 6]. According to the common structural approach, a computerized system should be able to classify and discriminate overlapping nuclei before feature extraction.…”

Multiple Feature Extraction from Cervical Cytology Images by Gaussian Mixture Model

“…When ratio of the area of nucleus to that of cytoplasm [10] [11] is less than 0.4 the cells are normal. When the ratio lies between 0.4 and 0.5, then the cells are classified as Cervical Intraepithelial Neoplasia1 LSIL.…”