Shape-based Features Investigation for Preneoplastic Lesions on Cervical Cancer Diagnosis

Daniela Terra, Daniela Terra, Adriano Lisboa, Mariana T. Rezende, Claudia Carneiro, Andrea Bianchi

2023

Abstract

The diagnosis of cervical lesions is an interpretative process carried out by specialists based on cellular information from the nucleus and cytoplasm. Some authors have used cell nucleus detection and segmentation algorithms to support the computer-assisted diagnosis process. These approaches are based on the assumption that the nucleus contains the most important information for lesion detection. This work investigates the influence of morphological information from the nucleus, cytoplasm, and both on cervical cell diagnosis. Experiments were performed to analyze 3,233 real cells extracting from each one 200 attributes related to size, shape, and edge contours. Results showed that morphological attributes could accurately represent lesions in binary and ternary classifications. However, identifying specific cell anomalies like Bethesda System classes requires adding new attributes such as texture.

Paper Citation

in Harvard Style

Terra D., Lisboa A., T. Rezende M., Carneiro C. and Bianchi A. (2023). Shape-based Features Investigation for Preneoplastic Lesions on Cervical Cancer Diagnosis. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 506-513. DOI: 10.5220/0011900800003417

in Bibtex Style

@conference{visapp23,
author={Daniela Terra and Adriano Lisboa and Mariana T. Rezende and Claudia Carneiro and Andrea Bianchi},
title={Shape-based Features Investigation for Preneoplastic Lesions on Cervical Cancer Diagnosis},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={506-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011900800003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Shape-based Features Investigation for Preneoplastic Lesions on Cervical Cancer Diagnosis
SN - 978-989-758-634-7
AU - Terra D.
AU - Lisboa A.
AU - T. Rezende M.
AU - Carneiro C.
AU - Bianchi A.
PY - 2023
SP - 506
EP - 513
DO - 10.5220/0011900800003417
PB - SciTePress