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. 2021 Nov 27:2021:4208254.
doi: 10.1155/2021/4208254. eCollection 2021.

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

Hermali Silva  1 , Kalaivani Chellappan  2 , Nadira Karunaweera  1
Affiliations

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

Hermali Silva et al. Comput Math Methods Med. .

Abstract

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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Conflict of interest statement

The authors declare that they have no real or perceived conflicts of interests.

Figures

Figure 1
Figure 1
System block diagram of the agile integration technique for image processing and analysis.
Figure 2
Figure 2
Image analysis. (a–c) Histograms before thresholding. (d–f) Histograms after thresholding. (g–i) Images after applying thresholding.
Figure 3
Figure 3
Thresholding and contour analyses of leishmaniasis lesions.
Figure 4
Figure 4
Variance in the images of the IL-SSG sensitive and poorly responded lesions.
Figure 5
Figure 5
Image preparation–application of cropping algorithm to the raw RBG images taken from a smart phone.
Figure 6
Figure 6
Image preprocessing and image segmentation. (a–c) Luminosity image obtained from color space transformation of the cropped RBG image. (d–f) Corresponding grayscale images obtained after luminosity extraction and principal component analysis.
Figure 7
Figure 7
Color pattern distribution.
Figure 8
Figure 8
Signal spectrogram of the 10 ulcer images.
See this image and copyright information in PMC

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