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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| Veröffentlicht in: | Journal of healthcare engineering Jg. 2018; H. 2018; S. 1 - 13 |
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| Sprache: | Englisch |
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Cairo, Egypt
Hindawi Publishing Corporation
01.01.2018
Hindawi John Wiley & Sons, Inc |
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| ISSN: | 2040-2295, 2040-2309 |
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| Abstract | 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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| AbstractList | 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. 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 , 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 , 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; (area, axes, perimeter, and solidity), , and a set of (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 for educational (i.e., untrained physicians) and preventive assistance technology purposes. 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. 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.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. |
| Audience | Academic |
| Author | Vega Martínez, Gabriel Vera, A. Ramos Becerril, Francisco José Leija, L. Toledo-Peral, Cinthya Lourdes Gutiérrez-Martínez, Josefina |
| AuthorAffiliation | 1 División de Investigación en Ingeniería Médica, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México–Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, C.P. 14389 Ciudad de México, Mexico 3 Subdirección de Medicina del Deporte, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México–Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, C.P. 14389 Ciudad de México, Mexico 2 Servicio de Rehabilitación Cardiaca, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México–Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, C.P. 14389 Ciudad de México, Mexico 4 LAREMUS, Sección Bioelectrónica, Departamento de Ingeniería Eléctrica, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, C.P. 07360 Ciudad de México, Mexico |
| AuthorAffiliation_xml | – name: 3 Subdirección de Medicina del Deporte, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México–Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, C.P. 14389 Ciudad de México, Mexico – name: 4 LAREMUS, Sección Bioelectrónica, Departamento de Ingeniería Eléctrica, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, C.P. 07360 Ciudad de México, Mexico – name: 1 División de Investigación en Ingeniería Médica, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México–Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, C.P. 14389 Ciudad de México, Mexico – name: 2 Servicio de Rehabilitación Cardiaca, Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Calz. México–Xochimilco No. 289, Col. Arenal de Guadalupe, Tlalpan, C.P. 14389 Ciudad de México, Mexico |
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| Cites_doi | 10.1007/s00125-011-2372-5 10.4103/2230-8210.129115 10.2337/diacare.21.5.855 10.1155/2015/496202 10.1371/journal.pone.0029740 10.1016/j.clinph.2006.05.007 10.1016/j.procs.2015.07.362 10.1111/j.1610-0387.2006.06123.x 10.1371/journal.pone.0076212 10.1371/journal.pone.0125578 |
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| Copyright | Copyright © 2018 Cinthya Lourdes Toledo Peral et al. COPYRIGHT 2018 John Wiley & Sons, Inc. Copyright © 2018 Cinthya Lourdes Toledo Peral et al. 2018 |
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| SubjectTerms | Algorithms Care and treatment Color Computer Graphics Diabetes Complications - diagnostic imaging Diabetes Complications - pathology Diabetes Mellitus, Type 2 - complications Diabetes Mellitus, Type 2 - diagnostic imaging Diabetic foot Diabetic Foot - complications Diabetics Humans Image processing Image Processing, Computer-Assisted - methods Leg - diagnostic imaging Leg - pathology Neural Networks, Computer Photography Pigmentation Disorders - diagnostic imaging Pigmentation Disorders - pathology Principal Component Analysis Purpura - pathology Skin Skin - diagnostic imaging Skin - pathology Skin Abnormalities - diagnostic imaging Skin Pigmentation Software User-Computer Interface |
| Title | An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes |
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