Image analysis for a mobile phone-based assessment of latent tuberculosis infection
The current, most widely used method to screen for latent tuberculosis infection is the Mantoux tuberculin skin test, where tuberculin is injected into a patient's arm and may result in a cutaneous induration forming at the site of injection. A diameter measurement of the resultant induration, recorded using a ruler and ball point pen, is currently used to indicate the presence of latent tuberculosis infection. Limitations associated with the tuberculin skin test procedure are the crudeness of the induration measurement method, the follow-up clinical visit required from patients to have their induration measured, and the need for trained clinicians who can perform the induration measurement. These limitations motivated research into a mobile phone-based screening system which can be used to obtain a more accurate measurement of the induration without the need for a second visit to the clinic by patients. The prototype screening tool consists of a user interface for capturing induration images and a backend processing system that produces a threedimensional reconstruction of the induration for measurement. Recommendations from previous studies on the prototype screening tool, which involved evaluation of the mobile application using mock induration images, included improving the accuracy of measuring the induration and evaluating the tool on real induration images. The aim of this study was to evaluate the existing backend system and explore an alternative assessment approach for assessing the induration. This was achieved through the following objectives: (1) applying the current backend system to real induration images, (2) examining the need for three-dimensional reconstruction for delineation of the induration for measurement and (3) exploring an alternative method for the assessment of induration images using deep learning. Results for the first objective showed the three-dimensional reconstruction to be unsuccessful on real images. This was due to the homogeneity between the indurations and the surrounding skin, rendering the algorithm ineffective in delineating the indurations to obtain the diameter measurement required for diagnosis. The second objective involved determining whether the image orientation or induration height affected the diagnostic measurement. It was found that real indurations are much flatter and more subtle compared to the mock indurations used in the previous studies. This motivated an alternative image assessment approach using deep learning. However, deep learning approaches require large databases of annotated images to prevent overfitting on training data. The last objective therefore involved the design and implementation of a generative adversarial network for generation of synthetic images from a limited number of real images, which allowed the generation of an unlimited number of realistic-looking synthetic images from 150 real induration images.
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