Cargando…

Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Se...

Descripción completa

Detalles Bibliográficos
Autores principales: Bayareh-Mancilla, Rafael, Medina-Ramos, Luis Alberto, Toriz-Vázquez, Alfonso, Hernández-Rodríguez, Yazmín Mariela, Cigarroa-Mayorga, Oscar Eduardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670641/
https://www.ncbi.nlm.nih.gov/pubmed/37998576
http://dx.doi.org/10.3390/diagnostics13223440
_version_ 1785149334635937792
author Bayareh-Mancilla, Rafael
Medina-Ramos, Luis Alberto
Toriz-Vázquez, Alfonso
Hernández-Rodríguez, Yazmín Mariela
Cigarroa-Mayorga, Oscar Eduardo
author_facet Bayareh-Mancilla, Rafael
Medina-Ramos, Luis Alberto
Toriz-Vázquez, Alfonso
Hernández-Rodríguez, Yazmín Mariela
Cigarroa-Mayorga, Oscar Eduardo
author_sort Bayareh-Mancilla, Rafael
collection PubMed
description Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.
format Online
Article
Text
id pubmed-10670641
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-106706412023-11-14 Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images Bayareh-Mancilla, Rafael Medina-Ramos, Luis Alberto Toriz-Vázquez, Alfonso Hernández-Rodríguez, Yazmín Mariela Cigarroa-Mayorga, Oscar Eduardo Diagnostics (Basel) Article Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians. MDPI 2023-11-14 /pmc/articles/PMC10670641/ /pubmed/37998576 http://dx.doi.org/10.3390/diagnostics13223440 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Bayareh-Mancilla, Rafael
Medina-Ramos, Luis Alberto
Toriz-Vázquez, Alfonso
Hernández-Rodríguez, Yazmín Mariela
Cigarroa-Mayorga, Oscar Eduardo
Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images
title Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images
title_full Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images
title_fullStr Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images
title_full_unstemmed Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images
title_short Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images
title_sort automated computer-assisted medical decision-making system based on morphological shape and skin thickness analysis for asymmetry detection in mammographic images
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670641/
https://www.ncbi.nlm.nih.gov/pubmed/37998576
http://dx.doi.org/10.3390/diagnostics13223440
work_keys_str_mv AT bayarehmancillarafael automatedcomputerassistedmedicaldecisionmakingsystembasedonmorphologicalshapeandskinthicknessanalysisforasymmetrydetectioninmammographicimages
AT medinaramosluisalberto automatedcomputerassistedmedicaldecisionmakingsystembasedonmorphologicalshapeandskinthicknessanalysisforasymmetrydetectioninmammographicimages
AT torizvazquezalfonso automatedcomputerassistedmedicaldecisionmakingsystembasedonmorphologicalshapeandskinthicknessanalysisforasymmetrydetectioninmammographicimages
AT hernandezrodriguezyazminmariela automatedcomputerassistedmedicaldecisionmakingsystembasedonmorphologicalshapeandskinthicknessanalysisforasymmetrydetectioninmammographicimages
AT cigarroamayorgaoscareduardo automatedcomputerassistedmedicaldecisionmakingsystembasedonmorphologicalshapeandskinthicknessanalysisforasymmetrydetectioninmammographicimages