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An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes

Diabetic skin manifestations, previous to ulcers and wounds, are not highly accounted as part of diagnosis even when they represent the first symptom of vascular damage and are present in up to 70% of patients with diabetes mellitus type II. Here, an application for skin macules characterization bas...

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Autores principales: Toledo Peral, Cinthya Lourdes, Ramos Becerril, Francisco José, Vega Martínez, Gabriel, Vera Hernández, Arturo, Leija Salas, Lorenzo, Gutiérrez Martínez, Josefina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311831/
https://www.ncbi.nlm.nih.gov/pubmed/30651950
http://dx.doi.org/10.1155/2018/9397105
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author Toledo Peral, Cinthya Lourdes
Ramos Becerril, Francisco José
Vega Martínez, Gabriel
Vera Hernández, Arturo
Leija Salas, Lorenzo
Gutiérrez Martínez, Josefina
author_facet Toledo Peral, Cinthya Lourdes
Ramos Becerril, Francisco José
Vega Martínez, Gabriel
Vera Hernández, Arturo
Leija Salas, Lorenzo
Gutiérrez Martínez, Josefina
author_sort Toledo Peral, Cinthya Lourdes
collection PubMed
description Diabetic skin manifestations, previous to ulcers and wounds, are not highly accounted as part of diagnosis even when they represent the first symptom of vascular damage and are present in up to 70% of patients with diabetes mellitus type II. Here, an application for skin macules characterization based on a three-stage segmentation and characterization algorithm used to classify vascular, petechiae, trophic changes, and trauma macules from digital photographs of the lower limbs is presented. First, in order to find the skin region, a logical multiplication is performed on two skin masks obtained from color space transformations; dynamic thresholds are stabilised to self-adjust to a variety of skin tones. Then, in order to locate the lesion region, illumination enhancement is performed using a chromatic model color space, followed by a principal component analysis gray-scale transformation. Finally, characteristics of each type of macule are considered and classified; morphologic properties (area, axes, perimeter, and solidity), intensity properties, and a set of shade indices (red, green, blue, and brown) are proposed as a measure to obviate skin color differences among subjects. The values calculated show differences between macules with a statistical significance, which agree with the physician's diagnosis. Later, macule properties are fed to an artificial neural network classifier, which proved a 97.5% accuracy, to differentiate between them. Characterization is useful in order to track macule changes and development along time, provides meaningful information to provide early treatments, and offers support in the prevention of amputations due to diabetic feet. A graphical user interface was designed to show the properties of the macules; this application could be the background of a future Diagnosis Assistance Tool for educational (i.e., untrained physicians) and preventive assistance technology purposes.
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spelling pubmed-63118312019-01-16 An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes Toledo Peral, Cinthya Lourdes Ramos Becerril, Francisco José Vega Martínez, Gabriel Vera Hernández, Arturo Leija Salas, Lorenzo Gutiérrez Martínez, Josefina J Healthc Eng Research Article Diabetic skin manifestations, previous to ulcers and wounds, are not highly accounted as part of diagnosis even when they represent the first symptom of vascular damage and are present in up to 70% of patients with diabetes mellitus type II. Here, an application for skin macules characterization based on a three-stage segmentation and characterization algorithm used to classify vascular, petechiae, trophic changes, and trauma macules from digital photographs of the lower limbs is presented. First, in order to find the skin region, a logical multiplication is performed on two skin masks obtained from color space transformations; dynamic thresholds are stabilised to self-adjust to a variety of skin tones. Then, in order to locate the lesion region, illumination enhancement is performed using a chromatic model color space, followed by a principal component analysis gray-scale transformation. Finally, characteristics of each type of macule are considered and classified; morphologic properties (area, axes, perimeter, and solidity), intensity properties, and a set of shade indices (red, green, blue, and brown) are proposed as a measure to obviate skin color differences among subjects. The values calculated show differences between macules with a statistical significance, which agree with the physician's diagnosis. Later, macule properties are fed to an artificial neural network classifier, which proved a 97.5% accuracy, to differentiate between them. Characterization is useful in order to track macule changes and development along time, provides meaningful information to provide early treatments, and offers support in the prevention of amputations due to diabetic feet. A graphical user interface was designed to show the properties of the macules; this application could be the background of a future Diagnosis Assistance Tool for educational (i.e., untrained physicians) and preventive assistance technology purposes. Hindawi 2018-12-16 /pmc/articles/PMC6311831/ /pubmed/30651950 http://dx.doi.org/10.1155/2018/9397105 Text en Copyright © 2018 Cinthya Lourdes Toledo Peral et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Toledo Peral, Cinthya Lourdes
Ramos Becerril, Francisco José
Vega Martínez, Gabriel
Vera Hernández, Arturo
Leija Salas, Lorenzo
Gutiérrez Martínez, Josefina
An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes
title An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes
title_full An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes
title_fullStr An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes
title_full_unstemmed An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes
title_short An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes
title_sort application for skin macules characterization based on a 3-stage image-processing algorithm for patients with diabetes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311831/
https://www.ncbi.nlm.nih.gov/pubmed/30651950
http://dx.doi.org/10.1155/2018/9397105
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