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Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study

Skin lesions are a feature of many diseases including cutaneous leishmaniasis (CL). Ulcerative lesions are a common manifestation of CL. Response to treatment in such lesions is judged through the assessment of the healing process by regular clinical observations, which remains a challenge for the c...

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Autores principales: Silva, Hermali, Chellappan, Kalaivani, Karunaweera, Nadira
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643237/
https://www.ncbi.nlm.nih.gov/pubmed/34873414
http://dx.doi.org/10.1155/2021/4208254
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author Silva, Hermali
Chellappan, Kalaivani
Karunaweera, Nadira
author_facet Silva, Hermali
Chellappan, Kalaivani
Karunaweera, Nadira
author_sort Silva, Hermali
collection PubMed
description Skin lesions are a feature of many diseases including cutaneous leishmaniasis (CL). Ulcerative lesions are a common manifestation of CL. Response to treatment in such lesions is judged through the assessment of the healing process by regular clinical observations, which remains a challenge for the clinician, health system, and the patient in leishmaniasis endemic countries. In this study, image processing was initially done using 40 CL lesion color images that were captured using a mobile phone camera, to establish a technique to extract features from the image which could be related to the clinical status of the lesion. The identified techniques were further developed, and ten ulcer images were analyzed to detect the extent of inflammatory response and/or signs of healing using pattern recognition of inflammatory tissue captured in the image. The images were preprocessed at the outset, and the quality was improved using the CIE L∗a∗b color space technique. Furthermore, features were extracted using the principal component analysis and profiled using the signal spectrogram technique. This study has established an adaptive thresholding technique ranging between 35 and 200 to profile the skin lesion images using signal spectrogram plotted using Signal Analyzer in MATLAB. The outcome indicates its potential utility in visualizing and assessing inflammatory tissue response in a CL ulcer. This approach is expected to be developed further to a mHealth-based prediction algorithm to enable remote monitoring of treatment response of cutaneous leishmaniasis.
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spelling pubmed-86432372021-12-05 Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study Silva, Hermali Chellappan, Kalaivani Karunaweera, Nadira Comput Math Methods Med Research Article Skin lesions are a feature of many diseases including cutaneous leishmaniasis (CL). Ulcerative lesions are a common manifestation of CL. Response to treatment in such lesions is judged through the assessment of the healing process by regular clinical observations, which remains a challenge for the clinician, health system, and the patient in leishmaniasis endemic countries. In this study, image processing was initially done using 40 CL lesion color images that were captured using a mobile phone camera, to establish a technique to extract features from the image which could be related to the clinical status of the lesion. The identified techniques were further developed, and ten ulcer images were analyzed to detect the extent of inflammatory response and/or signs of healing using pattern recognition of inflammatory tissue captured in the image. The images were preprocessed at the outset, and the quality was improved using the CIE L∗a∗b color space technique. Furthermore, features were extracted using the principal component analysis and profiled using the signal spectrogram technique. This study has established an adaptive thresholding technique ranging between 35 and 200 to profile the skin lesion images using signal spectrogram plotted using Signal Analyzer in MATLAB. The outcome indicates its potential utility in visualizing and assessing inflammatory tissue response in a CL ulcer. This approach is expected to be developed further to a mHealth-based prediction algorithm to enable remote monitoring of treatment response of cutaneous leishmaniasis. Hindawi 2021-11-27 /pmc/articles/PMC8643237/ /pubmed/34873414 http://dx.doi.org/10.1155/2021/4208254 Text en Copyright © 2021 Hermali Silva et al. https://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
Silva, Hermali
Chellappan, Kalaivani
Karunaweera, Nadira
Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
title Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
title_full Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
title_fullStr Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
title_full_unstemmed Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
title_short Image Processing for mHealth-Based Approach to Detect the Local Tissue Inflammation in Cutaneous Leishmaniasis: A Proof of Concept Study
title_sort image processing for mhealth-based approach to detect the local tissue inflammation in cutaneous leishmaniasis: a proof of concept study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8643237/
https://www.ncbi.nlm.nih.gov/pubmed/34873414
http://dx.doi.org/10.1155/2021/4208254
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