Automated Mobile Image Acquisition of Macroscopic Dermatological Lesions

The incidence of skin cancer has been rising every year translating in high economic costs. The development of mobile teledermatology applications that can contribute for the standardization of image acquisition can facilitate early diagnosis. This paper presents a new methodology for real-time automated image acquisition of macroscopic skin images via mobile devices. It merges an automated image focus assessment that uses a feature-based machine learning approach with segmentation of dermatological lesions using computer vision techniques. It also describes the datasets used to develop and evaluate the proposed approach: 3428 images from one dataset purposely collected using different mobile devices for the focus assessment component, and a total of 1380 images from two other datasets available on the literature to develop the segmentation approach. The best model for automatic focus assessment of preview images and acquired picture achieved an overall accuracy of 88.3% and 86.8%, respectively. The segmentation approach attained a Jaccard index of 85.81% and 68.59% for SMARTSKINS and Dermofit datasets, respectively. The developed algorithms present a fast processing time that is suitable for real-time usage in medium and high performance smartphones. These findings were also validated by implementing the proposed methodology within an android application demonstrating promising results.

Automated summary

This study presents a new methodology for real-time automated image acquisition of skin images via mobile devices. The developed algorithms achieved high accuracy in image focus assessment and segmentation, with promising results for real-time usage in medium and high performance smartphones.