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

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.

Diagnostic Tools for Cutaneous Leishmaniasis Caused by Leishmania donovani: A Narrative Review

“…A proof-of-concept study conducted in Sri Lanka utilizing photographic imaging has showcased the promising potential of novel methods, particularly for predicting lesion response to anti-leishmanial treatment. It is important to note that while this innovative approach has not yet been implemented in routine clinical practice in Sri Lanka, its demonstrated potential suggests it could be a valuable addition to the arsenal of tools for managing CL in the future [ 103 ]. Moreover, establishing a comprehensive image library of atypical CL lesions would further enhance their practical applications.…”

Jumping particle swarm optimization algorithm framework for content-based image retrieval system

“…The choice of feature extraction technique depends on the characteristics of the image database, the specific requirements of the CBIR system, and the nature of the objects or content being retrieved. It is common to employ a combination of different feature extraction techniques to capture various aspects of the images and enhance the retrieval performance [19].…”