An ensemble method for nuclei detection of overlapping cervical cells

The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. T...

Full description

Saved in:
Bibliographic Details
Published in:Expert systems with applications Vol. 185; p. 115642
Main Authors: Diniz, Débora Nasser, Vitor, Rafael Ferreira, Bianchi, Andrea Gomes Campos, Delabrida, Saul, Carneiro, Cláudia Martins, Ushizima, Daniela Mayumi, de Medeiros, Fátima Nelsizeuma Sombra, Souza, Marcone Jamilson Freitas
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 15.12.2021
Elsevier BV
Elsevier
Subjects:
Abnormalities
Algorithms
Centroids
Cervical cancer
Cytology
Decision trees
Detection approach
Ensemble methods
Feature extraction
Image processing algorithm
Image segmentation
MATHEMATICS AND COMPUTING
Nuclei (cytology)
Pap smear test
Recall
Detection approach
Ensemble methods
Image processing algorithm
Cervical cancer
Pap smear test
ISSN:0957-4174, 1873-6793
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Pap test is a preventive approach that requires specialized and labor-intensive examination of cytological preparations to track potentially cancerous cells from the internal and external cervix surface. A cytopathologist must analyze many microscopic fields while screening for abnormal cells. Therefore there is hope that a support decision system could assist with clinical diagnosis, for example, by identifying sub-cellular abnormalities, such as changes in the nuclei features. This work proposes an ensemble method for cervical nuclei detection aiming to reduce the workload of cytopathologists. First, a preprocessing phase divides the original image into superpixels, which are input to feature extraction and selection algorithms. The proposed ensemble method combines three classifiers: Decision Tree (DT), Nearest Centroid (NC), and k-Nearest Neighbors (k-NN), which are evaluated against the ISBI’14 Overlapping Cervical Cytology Image Segmentation Challenge dataset. Experiments show that the proposed method is the state-of-the-art algorithm of the literature for recall (0.999) and F1 values (0.993). It produced a recall very close to the optimum value and also kept high precision (0.988). [Display omitted] •Ensemble of classifiers to provide a more assertive cervical cell nuclei segmentation.•Automatic inspection of Pap smear test to reduce the human workload in cervical cell analysis.•Superpixel computation and feature extraction to improve cell characterization and classification.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
AC02-05CH11231
USDOE Office of Science (SC)
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115642